Binary files /tmp/tmpCzhIkg/Ve8CD1Uy2q/r-bioc-gosemsim-2.14.2/build/vignette.rds and /tmp/tmpCzhIkg/80funZGYjP/r-bioc-gosemsim-2.16.1/build/vignette.rds differ
diff -Nru r-bioc-gosemsim-2.14.2/debian/changelog r-bioc-gosemsim-2.16.1/debian/changelog
--- r-bioc-gosemsim-2.14.2/debian/changelog	2020-09-08 19:21:54.000000000 +0000
+++ r-bioc-gosemsim-2.16.1/debian/changelog	2020-11-03 09:25:38.000000000 +0000
@@ -1,3 +1,10 @@
+r-bioc-gosemsim (2.16.1-1) unstable; urgency=medium
+
+  * Team upload.
+  * New upstream version
+
+ -- Dylan Aïssi <daissi@debian.org>  Tue, 03 Nov 2020 10:25:38 +0100
+
 r-bioc-gosemsim (2.14.2-1) unstable; urgency=medium
 
   * New upstream version
diff -Nru r-bioc-gosemsim-2.14.2/debian/copyright r-bioc-gosemsim-2.16.1/debian/copyright
--- r-bioc-gosemsim-2.14.2/debian/copyright	2020-09-08 19:21:54.000000000 +0000
+++ r-bioc-gosemsim-2.16.1/debian/copyright	2020-11-03 09:25:38.000000000 +0000
@@ -10,45 +10,6 @@
  Lluís Revilla Sancho
 License: Artistic-2.0
 
-Files: vignettes/nature.csl
-Copyright: 2012-2014 Michael Berkowitz <mberkowi@gmu.edu>
-License: CC-BY-SA-3.0
- This work is licensed under a Creative Commons Attribution-ShareAlike 3.0 License
- http://creativecommons.org/licenses/by-sa/3.0/
- .
- This is a human-readable summary of (and not a substitute for) the license.
- Disclaimer.
- .
- You are free to:
- .
-  • Share — copy and redistribute the material in any medium or format
-  • Adapt — remix, transform, and build upon the material
-  • for any purpose, even commercially.
-  •
- .
- This license is acceptable for Free Cultural Works.
- .
-  • The licensor cannot revoke these freedoms as long as you follow the license
-    terms.
- .
- ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
- .
- Under the following terms:
- .
-  • Attribution — You must give appropriate credit, provide a link to the
-    license, and indicate if changes were made. You may do so in any reasonable
-    manner, but not in any way that suggests the licensor endorses you or your
-    use.
- .
-    Attribute this work:
- .
-  • ShareAlike — If you remix, transform, or build upon the material, you must
-    distribute your contributions under the same license as the original.
- .
-  • No additional restrictions — You may not apply legal terms or technological
-    measures that legally restrict others from doing anything the license
-    permits.
-
 Files: debian/*
 Copyright: 2020 Andreas Tille <tille@debian.org>
 License: Artistic-2.0
diff -Nru r-bioc-gosemsim-2.14.2/DESCRIPTION r-bioc-gosemsim-2.16.1/DESCRIPTION
--- r-bioc-gosemsim-2.14.2/DESCRIPTION	2020-09-04 21:48:51.000000000 +0000
+++ r-bioc-gosemsim-2.16.1/DESCRIPTION	2020-10-30 00:15:31.000000000 +0000
@@ -1,7 +1,7 @@
 Package: GOSemSim
 Type: Package
 Title: GO-terms Semantic Similarity Measures
-Version: 2.14.2
+Version: 2.16.1
 Authors@R: c( person(given = "Guangchuang", family = "Yu",             email = "guangchuangyu@gmail.com", role  = c("aut", "cre")),
               person(given = "Alexey",      family = "Stukalov",       email = "astukalov@gmail.com",     role  = "ctb"),
               person(given = "Chuanle",     family = "Xiao",           email = "xiaochuanle@126.com",     role  = "ctb"),
@@ -23,16 +23,16 @@
 ByteCompile: true
 License: Artistic-2.0
 Encoding: UTF-8
-URL: https://guangchuangyu.github.io/software/GOSemSim
+URL: https://yulab-smu.top/biomedical-knowledge-mining-book/
 BugReports: https://github.com/YuLab-SMU/GOSemSim/issues
-Packaged: 2020-09-04 21:48:51 UTC; biocbuild
+Packaged: 2020-10-30 00:15:31 UTC; biocbuild
 biocViews: Annotation, GO, Clustering, Pathways, Network, Software
 RoxygenNote: 7.1.1
 git_url: https://git.bioconductor.org/packages/GOSemSim
-git_branch: RELEASE_3_11
-git_last_commit: 15b32bb
-git_last_commit_date: 2020-09-04
-Date/Publication: 2020-09-04
+git_branch: RELEASE_3_12
+git_last_commit: 92f1d56
+git_last_commit_date: 2020-10-29
+Date/Publication: 2020-10-29
 NeedsCompilation: yes
 Author: Guangchuang Yu [aut, cre],
   Alexey Stukalov [ctb],
diff -Nru r-bioc-gosemsim-2.14.2/inst/doc/GOSemSim.html r-bioc-gosemsim-2.16.1/inst/doc/GOSemSim.html
--- r-bioc-gosemsim-2.14.2/inst/doc/GOSemSim.html	2020-09-04 21:48:51.000000000 +0000
+++ r-bioc-gosemsim-2.16.1/inst/doc/GOSemSim.html	2020-10-30 00:15:30.000000000 +0000
@@ -10,110 +10,71 @@
 
 <meta name="viewport" content="width=device-width, initial-scale=1" />
 
-<meta name="author" content=" Guangchuang Yu School of Basic Medical Sciences, Southern Medical University" />
 
-<meta name="date" content="2020-09-04" />
+<meta name="date" content="2020-10-29" />
 
 <title>GO Semantic Similarity Analysis</title>
 
+<script>// Hide empty <a> tag within highlighted CodeBlock for screen reader accessibility (see https://github.com/jgm/pandoc/issues/6352#issuecomment-626106786) -->
+// v0.0.1
+// Written by JooYoung Seo (jooyoung@psu.edu) and Atsushi Yasumoto on June 1st, 2020.
+
+document.addEventListener('DOMContentLoaded', function() {
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+    for (var j = 0; j < linkList.length; j++) {
+      if (linkList[j].innerHTML === "") {
+        linkList[j].setAttribute('aria-hidden', 'true');
+      }
+    }
+  }
+});
+</script>
+<style type="text/css">
+a.anchor-section {margin-left: 10px; visibility: hidden; color: inherit;}
+a.anchor-section::before {content: '#';}
+.hasAnchor:hover a.anchor-section {visibility: visible;}
+</style>
+<script>// Anchor sections v1.0 written by Atsushi Yasumoto on Oct 3rd, 2020.
+document.addEventListener('DOMContentLoaded', function() {
+  // Do nothing if AnchorJS is used
+  if (typeof window.anchors === 'object' && anchors.hasOwnProperty('hasAnchorJSLink')) {
+    return;
+  }
 
+  const h = document.querySelectorAll('h1, h2, h3, h4, h5, h6');
 
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+    x.appendChild(anchor);
+  });
+});
 </script>
 
 
 
+
+
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@@ -138,322 +99,40 @@
 
 <section class="page-header">
 <h1 class="title toc-ignore project-name">GO Semantic Similarity Analysis</h1>
-<h4 class="author project-author"><br />
-Guangchuang Yu<br />
-School of Basic Medical Sciences, Southern Medical University</h4>
-<h4 class="date project-date">2020-09-04</h4>
+<h4 class="author project-author">Guangchuang Yu</h4>
+<address class="author_afil">
+Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University<br><a class="author_email" href="mailto:#"><a href="mailto:guangchuangyu@gmail.com" class="email">guangchuangyu@gmail.com</a></a>
+</address>
+<h4 class="date project-date">2020-10-29</h4>
 </section>
 
 
 <div id="TOC" class="toc">
 <div class="toc-box">
 <ul>
+<li><a href="#vignette">Vignette</a></li>
 <li><a href="#citation">Citation</a></li>
-<li><a href="#introduction">Introduction</a></li>
-<li><a href="#semantic-similarity-measurement-based-on-go">Semantic Similarity Measurement Based on GO</a><ul>
-<li><a href="#information-content-based-methods">Information content-based methods</a><ul>
-<li><a href="#resnik-method">Resnik method</a></li>
-<li><a href="#lin-method">Lin method</a></li>
-<li><a href="#rel-method">Rel method</a></li>
-<li><a href="#jiang-method">Jiang method</a></li>
-</ul></li>
-<li><a href="#graph-based-method">Graph-based method</a><ul>
-<li><a href="#wang-method">Wang method</a></li>
-</ul></li>
-<li><a href="#supported-organisms">Supported organisms</a></li>
-<li><a href="#gosim-and-mgosim-function">goSim and mgoSim function</a></li>
-</ul></li>
-<li><a href="#gene-semantic-similarity-measurement">Gene Semantic Similarity Measurement</a><ul>
-<li><a href="#combine-methods">Combine methods</a><ul>
-<li><a href="#max">max</a></li>
-<li><a href="#avg">avg</a></li>
-<li><a href="#rcmax">rcmax</a></li>
-<li><a href="#bma">BMA</a></li>
-</ul></li>
-<li><a href="#genesim-and-mgenesim">geneSim and mgeneSim</a></li>
-<li><a href="#clustersim-and-mclustersim">clusterSim and mclusterSim</a></li>
-</ul></li>
-<li><a href="#applications">Applications</a></li>
-<li><a href="#go-enrichment-analysis">GO enrichment analysis</a></li>
-<li><a href="#disease-ontology-semantic-and-enrichment-analysis">Disease Ontology Semantic and Enrichment analysis</a></li>
-<li><a href="#mesh-enrichment-and-semantic-analyses">MeSH enrichment and semantic analyses</a></li>
 <li><a href="#need-helps">Need helps?</a></li>
-<li><a href="#session-information">Session Information</a></li>
-<li><a href="#references">References</a></li>
 </ul>
 </div>
 </div>
 
 <section class="main-content">
+<div id="vignette" class="section level1">
+<h1>Vignette</h1>
+<p>Please go to <a href="https://yulab-smu.top/biomedical-knowledge-mining-book/" class="uri">https://yulab-smu.top/biomedical-knowledge-mining-book/</a> for the full vignette.</p>
+</div>
 <div id="citation" class="section level1">
 <h1>Citation</h1>
-<p>If you use <a href="http://bioconductor.org/packages/GOSemSim">GOSemSim</a> in published research, please cite:</p>
-<ul>
+<p>If you use <a href="http://bioconductor.org/packages/GOSemSim">GOSemSim</a> in published research, please cite the most appropriate paper(s) from this list:</p>
+<ol style="list-style-type: decimal">
 <li><strong>Yu G</strong>. <a href="http://dx.doi.org/10.1007/978-1-0716-0301-7_11">Gene Ontology Semantic Similarity Analysis Using GOSemSim</a>. In: Kidder B. (eds) Stem Cell Transcriptional Networks. <strong><em>Methods in Molecular Biology</em></strong>, 2020, 2117:207-215. Humana, New York, NY.</li>
 <li><strong>Yu G</strong><sup>#</sup>, Li F<sup>#</sup>, Qin Y, Bo X<sup>*</sup>, Wu Y and Wang S<sup>*</sup>. <a href="http://dx.doi.org/10.1093/bioinformatics/btq064">GOSemSim: an R package for measuring semantic similarity among GO terms and gene products</a>. <strong><em>Bioinformatics</em></strong>. 2010, 26(7):976-978.</li>
-</ul>
-</div>
-<div id="introduction" class="section level1">
-<h1>Introduction</h1>
-<p>Functional similarity of gene products can be estimated by controlled biological vocabularies, such as Gene Ontology (GO) and Disease Ontology (DO). GO comprises of three orthogonal ontologies, i.e. molecular function (MF), biological process (BP), and cellular component (CC).</p>
-<p>Four methods including Resnik<span class="citation">(Philip 1999)</span>, Jiang<span class="citation">(Jiang and Conrath 1997)</span>, Lin<span class="citation">(Lin 1998)</span> and Schlicker<span class="citation">(Schlicker et al. 2006)</span> have been presented to determine the semantic similarity of two GO terms based on the annotation statistics of their common ancestor terms. Wang<span class="citation">(Wang et al. 2007)</span> proposed a method to measure the similarity based on the graph structure of GO. Each of these methods has its own advantages and weaknesses.</p>
-<p><a href="https://www.bioconductor.org/packages/GOSemSim">GOSemSim</a> package<span class="citation">(Yu et al. 2010)</span> is developed to compute semantic similarity among GO terms, sets of GO terms, gene products, and gene clusters, providing five methods mentioned above. We have developed another package, <a href="https://www.bioconductor.org/packages/DOSE">DOSE</a><span class="citation">(Yu et al. 2015)</span>, for measuring semantic similarity among Disease Ontology (DO) terms and gene products at disease perspective.</p>
-</div>
-<div id="semantic-similarity-measurement-based-on-go" class="section level1">
-<h1>Semantic Similarity Measurement Based on GO</h1>
-<div id="information-content-based-methods" class="section level2">
-<h2>Information content-based methods</h2>
-<p>Four methods proposed by Resnik<span class="citation">(Philip 1999)</span>, Jiang<span class="citation">(Jiang and Conrath 1997)</span>, Lin<span class="citation">(Lin 1998)</span> and Schlicker<span class="citation">(Schlicker et al. 2006)</span> are information content (IC) based, which depend on the frequencies of two GO terms involved and that of their closest common ancestor term in a specific corpus of GO annotations. The information content of a GO term is computed by the negative log probability of the term occurring in GO corpus. A rarely used term contains a greater amount of information.</p>
-<p>The frequency of a term t is defined as: <span class="math inline">\(p(t) = \frac{n_{t'}}{N} | t' \in \left\{t, \; children\: of\: t \right\}\)</span></p>
-<p>where <span class="math inline">\(n_{t'}\)</span> is the number of term <span class="math inline">\(t'\)</span>, and <span class="math inline">\(N\)</span> is the total number of terms in GO corpus.</p>
-<p>Thus the information content is defined as: <span class="math inline">\(IC(t) = -\log(p(t))\)</span></p>
-<p>As GO allow multiple parents for each concept, two terms can share parents by multiple paths. IC-based methods calculate similarity of two GO terms based on the information content of their closest common ancestor term, which was also called most informative common ancestor (MICA).</p>
-<div id="resnik-method" class="section level3">
-<h3>Resnik method</h3>
-<p>The Resnik method is defined as: <span class="math inline">\(sim_{Resnik}(t_1,t_2) = IC(MICA)\)</span></p>
-</div>
-<div id="lin-method" class="section level3">
-<h3>Lin method</h3>
-<p>The Lin method is defined as: <span class="math inline">\(sim_{Lin}(t_1,t_2) = \frac{2IC(MICA)}{IC(t_1)+IC(t_2)}\)</span></p>
-</div>
-<div id="rel-method" class="section level3">
-<h3>Rel method</h3>
-<p>The Relevance method, which was proposed by Schlicker, combine Resnik’s and Lin’s method and is defined as: <span class="math inline">\(sim_{Rel}(t_1,t_2) = \frac{2IC(MICA)(1-p(MICA))}{IC(t_1)+IC(t_2)}\)</span></p>
-</div>
-<div id="jiang-method" class="section level3">
-<h3>Jiang method</h3>
-<p>The Jiang and Conrath’s method is defined as: <span class="math inline">\(sim_{Jiang}(t_1,t_2) = 1-\min(1, IC(t_1) + IC(t_2) - 2IC(MICA))\)</span></p>
-</div>
-</div>
-<div id="graph-based-method" class="section level2">
-<h2>Graph-based method</h2>
-<p>Graph-based methods using the topology of GO graph structure to compute semantic similarity. Formally, a GO term A can be represented as <span class="math inline">\(DAG_{A}=(A,T_{A},E_{A})\)</span> where <span class="math inline">\(T_{A}\)</span> is the set of GO terms in <span class="math inline">\(DAG_{A}\)</span>, including term A and all of its ancestor terms in the GO graph, and <span class="math inline">\(E_{A}\)</span> is the set of edges connecting the GO terms in <span class="math inline">\(DAG_{A}\)</span>.</p>
-<div id="wang-method" class="section level3">
-<h3>Wang method</h3>
-<p>To encode the semantic of a GO term in a measurable format to enable a quantitative comparison, Wang<span class="citation">(Wang et al. 2007)</span> firstly defined the semantic value of term A as the aggregate contribution of all terms in <span class="math inline">\(DAG_{A}\)</span> to the semantics of term A, terms closer to term A in <span class="math inline">\(DAG_{A}\)</span> contribute more to its semantics. Thus, defined the contribution of a GO term <span class="math inline">\(t\)</span> to the semantic of GO term <span class="math inline">\(A\)</span> as the S-value of GO term <span class="math inline">\(t\)</span> related to term <span class="math inline">\(A\)</span>. For any of term <span class="math inline">\(t\)</span> in <span class="math inline">\(DAG_{A}\)</span>, its S-value related to term <span class="math inline">\(A\)</span>, <span class="math inline">\(S_{A}(\textit{t})\)</span> is defined as:</p>
-<p><span class="math inline">\(\left\{\begin{array}{l} S_{A}(A)=1 \\ S_{A}(\textit{t})=\max\{w_{e} \times S_{A}(\textit{t}') | \textit{t}' \in children \: of(\textit{t}) \} \; if \: \textit{t} \ne A \end{array} \right.\)</span></p>
-<p>where <span class="math inline">\(w_{e}\)</span> is the semantic contribution factor for edge <span class="math inline">\(e \in E_{A}\)</span> linking term <span class="math inline">\(t\)</span> with its child term <span class="math inline">\(t'\)</span>. Term <span class="math inline">\(A\)</span> contributes to its own is defined as 1. After obtaining the S-values for all terms in <span class="math inline">\(DAG_{A}\)</span>, the semantic value of DO term A, <span class="math inline">\(SV(A)\)</span>, is calculated as:</p>
-<p><span class="math inline">\(SV(A)=\displaystyle\sum_{t \in T_{A}} S_{A}(t)\)</span></p>
-<p>Thus given two GO terms A and B, the semantic similarity between these two terms is defined as:</p>
-<p><span class="math inline">\(sim_{Wang}(A, B) = \frac{\displaystyle\sum_{t \in T_{A} \cap T_{B}}{S_{A}(t) + S_{B}(t)}}{SV(A) + SV(B)}\)</span></p>
-<p>where <span class="math inline">\(S_{A}(\textit{t})\)</span> is the S-value of GO term <span class="math inline">\(t\)</span> related to term <span class="math inline">\(A\)</span> and <span class="math inline">\(S_{B}(\textit{t})\)</span> is the S-value of GO term <span class="math inline">\(t\)</span> related to term <span class="math inline">\(B\)</span>.</p>
-<p>This method proposed by Wang<span class="citation">(Wang et al. 2007)</span> determines the semantic similarity of two GO terms based on both the locations of these terms in the GO graph and their relations with their ancestor terms.</p>
-</div>
-</div>
-<div id="supported-organisms" class="section level2">
-<h2>Supported organisms</h2>
-<p>For IC-based methods, information of GO term is species specific. We need to calculate <code>IC</code> for all GO terms of a species before we measure semantic similarity. <a href="https://www.bioconductor.org/packages/GOSemSim">GOSemSim</a> support all organisms that have an <code>OrgDb</code> object available.</p>
-<p>Bioconductor have already provided <code>OrgDb</code> for about 20 species, see <a href="http://bioconductor.org/packages/release/BiocViews.html#___OrgDb" class="uri">http://bioconductor.org/packages/release/BiocViews.html#___OrgDb</a>.</p>
-<p>We can build query <code>OrgDb</code> online via <a href="https://www.bioconductor.org/packages/AnnotationHub">AnnotationHub</a>. For example:</p>
-<div class="sourceCode" id="cb1"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb1-1" data-line-number="1"><span class="kw">library</span>(AnnotationHub)</a>
-<a class="sourceLine" id="cb1-2" data-line-number="2">hub &lt;-<span class="st"> </span><span class="kw">AnnotationHub</span>()</a>
-<a class="sourceLine" id="cb1-3" data-line-number="3">q &lt;-<span class="st"> </span><span class="kw">query</span>(hub, <span class="st">&quot;Cricetulus&quot;</span>)</a>
-<a class="sourceLine" id="cb1-4" data-line-number="4">id &lt;-<span class="st"> </span>q<span class="op">$</span>ah_id[<span class="kw">length</span>(q)]</a>
-<a class="sourceLine" id="cb1-5" data-line-number="5">Cgriseus &lt;-<span class="st"> </span>hub[[id]]</a></code></pre></div>
-<p>If organism is not supported by <a href="https://www.bioconductor.org/packages/AnnotationHub">AnnotationHub</a>, user can use <a href="https://www.bioconductor.org/packages/AnnotationForge">AnnotationForge</a> to build <code>OrgDb</code>.</p>
-<p>Once we have <code>OrgDb</code>, we can build annotation data needed by <a href="https://www.bioconductor.org/packages/GOSemSim">GOSemSim</a> via <code>godata</code> function.</p>
-<div class="sourceCode" id="cb2"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb2-1" data-line-number="1"><span class="kw">library</span>(GOSemSim)</a>
-<a class="sourceLine" id="cb2-2" data-line-number="2">hsGO &lt;-<span class="st"> </span><span class="kw">godata</span>(<span class="st">'org.Hs.eg.db'</span>, <span class="dt">ont=</span><span class="st">&quot;MF&quot;</span>)</a></code></pre></div>
-<p>User can set <code>computeIC=FALSE</code> if they only want to use Wang’s method.</p>
-</div>
-<div id="gosim-and-mgosim-function" class="section level2">
-<h2>goSim and mgoSim function</h2>
-<p>In <a href="https://www.bioconductor.org/packages/GOSemSim">GOSemSim</a>, we implemented all these IC-based and graph-based methods. <strong><em>goSim</em></strong> function calculates semantic similarity between two GO terms, while <strong><em>mgoSim</em></strong> function calculates semantic similarity between two sets of GO terms.</p>
-<div class="sourceCode" id="cb3"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb3-1" data-line-number="1"><span class="kw">goSim</span>(<span class="st">&quot;GO:0004022&quot;</span>, <span class="st">&quot;GO:0005515&quot;</span>, <span class="dt">semData=</span>hsGO, <span class="dt">measure=</span><span class="st">&quot;Jiang&quot;</span>)</a></code></pre></div>
-<pre><code>## [1] 0.159</code></pre>
-<div class="sourceCode" id="cb5"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb5-1" data-line-number="1"><span class="kw">goSim</span>(<span class="st">&quot;GO:0004022&quot;</span>, <span class="st">&quot;GO:0005515&quot;</span>, <span class="dt">semData=</span>hsGO, <span class="dt">measure=</span><span class="st">&quot;Wang&quot;</span>)</a></code></pre></div>
-<pre><code>## [1] 0.116</code></pre>
-<div class="sourceCode" id="cb7"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb7-1" data-line-number="1">go1 =<span class="st"> </span><span class="kw">c</span>(<span class="st">&quot;GO:0004022&quot;</span>,<span class="st">&quot;GO:0004024&quot;</span>,<span class="st">&quot;GO:0004174&quot;</span>)</a>
-<a class="sourceLine" id="cb7-2" data-line-number="2">go2 =<span class="st"> </span><span class="kw">c</span>(<span class="st">&quot;GO:0009055&quot;</span>,<span class="st">&quot;GO:0005515&quot;</span>)</a>
-<a class="sourceLine" id="cb7-3" data-line-number="3"><span class="kw">mgoSim</span>(go1, go2, <span class="dt">semData=</span>hsGO, <span class="dt">measure=</span><span class="st">&quot;Wang&quot;</span>, <span class="dt">combine=</span><span class="ot">NULL</span>)</a></code></pre></div>
-<pre><code>##            GO:0009055 GO:0005515
-## GO:0004022      0.368      0.116
-## GO:0004024      0.335      0.107
-## GO:0004174      0.663      0.119</code></pre>
-<div class="sourceCode" id="cb9"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb9-1" data-line-number="1"><span class="kw">mgoSim</span>(go1, go2, <span class="dt">semData=</span>hsGO, <span class="dt">measure=</span><span class="st">&quot;Wang&quot;</span>, <span class="dt">combine=</span><span class="st">&quot;BMA&quot;</span>)</a></code></pre></div>
-<pre><code>## [1] 0.43</code></pre>
-</div>
-</div>
-<div id="gene-semantic-similarity-measurement" class="section level1">
-<h1>Gene Semantic Similarity Measurement</h1>
-<p>On the basis of semantic similarity between GO terms, <a href="https://www.bioconductor.org/packages/GOSemSim">GOSemSim</a> can also compute semantic similarity among sets of GO terms, gene products, and gene clusters.</p>
-<p>Suppose we have gene <span class="math inline">\(g_1\)</span> annotated by GO terms sets <span class="math inline">\(GO_{1}=\{go_{11},go_{12} \cdots go_{1m}\}\)</span> and <span class="math inline">\(g_2\)</span> annotated by <span class="math inline">\(GO_{2}=\{go_{21},go_{22} \cdots go_{2n}\}\)</span>, <a href="https://www.bioconductor.org/packages/GOSemSim">GOSemSim</a> implemented four methods which called <strong><em>max</em></strong>, <strong><em>avg</em></strong>, <strong><em>rcmax</em></strong>, and <strong><em>BMA</em></strong> to combine semantic similarity scores of multiple GO terms. The similarities among gene products and gene clusters which annotated by multiple GO terms were also calculated by the same combine methods mentioned above.</p>
-<div id="combine-methods" class="section level2">
-<h2>Combine methods</h2>
-<div id="max" class="section level3">
-<h3>max</h3>
-<p>The <strong><em>max</em></strong> method calculates the maximum semantic similarity score over all pairs of GO terms between these two GO term sets.</p>
-<p><span class="math inline">\(sim_{max}(g_1, g_2) = \displaystyle\max_{1 \le i \le m, 1 \le j \le n} sim(go_{1i}, go_{2j})\)</span></p>
-</div>
-<div id="avg" class="section level3">
-<h3>avg</h3>
-<p>The <strong><em>avg</em></strong> calculates the average semantic similarity score over all pairs of GO terms.</p>
-<p><span class="math inline">\(sim_{avg}(g_1, g_2) = \frac{\displaystyle\sum_{i=1}^m\sum_{j=1}^nsim(go_{1i}, go_{2j})}{m \times n}\)</span></p>
-</div>
-<div id="rcmax" class="section level3">
-<h3>rcmax</h3>
-<p>Similarities among two sets of GO terms form a matrix, the <strong><em>rcmax</em></strong> method uses the maximum of <em>RowScore</em> and <em>ColumnScore</em>, where <em>RowScore</em> (or <em>ColumnScore</em>) is the average of maximum similarity on each row (or column).</p>
-<p><span class="math inline">\(sim_{rcmax}(g_1, g_2) = \max(\frac{\displaystyle\sum_{i=1}^m \max_{1 \le j \le n} sim(go_{1i}, go_{2j})}{m},\frac{\displaystyle\sum_{j=1}^n \max_{1 \le i \le m} sim(go_{1i},go_{2j})}{n})\)</span></p>
-</div>
-<div id="bma" class="section level3">
-<h3>BMA</h3>
-<p>The <strong><em>BMA</em></strong> method, used the <strong>B</strong>est-<strong>M</strong>atch <strong>A</strong>verage strategy, calculates the average of all maximum similarities on each row and column, and is defined as:</p>
-<p><span class="math inline">\(sim_{BMA}(g_1, g_2) = \frac{\displaystyle\sum_{1=i}^m \max_{1 \le j \le n}sim(go_{1i}, go_{2j}) + \displaystyle\sum_{1=j}^n \max_{1 \le i \le m}sim(go_{1i}, go_{2j})} {m+n}\)</span></p>
-</div>
-</div>
-<div id="genesim-and-mgenesim" class="section level2">
-<h2>geneSim and mgeneSim</h2>
-<p>In <a href="https://www.bioconductor.org/packages/GOSemSim">GOSemSim</a>, we implemented <strong><em>geneSim</em></strong> to calculate semantic similarity between two gene products, and <strong><em>mgeneSim</em></strong> to calculate semantic similarity among multiple gene products.</p>
-<div class="sourceCode" id="cb11"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb11-1" data-line-number="1"><span class="kw">geneSim</span>(<span class="st">&quot;241&quot;</span>, <span class="st">&quot;251&quot;</span>, <span class="dt">semData=</span>hsGO, <span class="dt">measure=</span><span class="st">&quot;Wang&quot;</span>, <span class="dt">combine=</span><span class="st">&quot;BMA&quot;</span>)</a></code></pre></div>
-<pre><code>## $geneSim
-## [1] 0.149
-## 
-## $GO1
-## [1] &quot;GO:0004364&quot; &quot;GO:0004464&quot; &quot;GO:0004602&quot; &quot;GO:0005515&quot; &quot;GO:0047485&quot;
-## [6] &quot;GO:0050544&quot;
-## 
-## $GO2
-## [1] &quot;GO:0004035&quot;</code></pre>
-<div class="sourceCode" id="cb13"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb13-1" data-line-number="1"><span class="kw">mgeneSim</span>(<span class="dt">genes=</span><span class="kw">c</span>(<span class="st">&quot;835&quot;</span>, <span class="st">&quot;5261&quot;</span>,<span class="st">&quot;241&quot;</span>, <span class="st">&quot;994&quot;</span>),</a>
-<a class="sourceLine" id="cb13-2" data-line-number="2">         <span class="dt">semData=</span>hsGO, <span class="dt">measure=</span><span class="st">&quot;Wang&quot;</span>,<span class="dt">verbose=</span><span class="ot">FALSE</span>)</a></code></pre></div>
-<pre><code>##        835  5261   241   994
-## 835  1.000 0.517 0.503 0.620
-## 5261 0.517 1.000 0.447 0.505
-## 241  0.503 0.447 1.000 0.452
-## 994  0.620 0.505 0.452 1.000</code></pre>
-<div class="sourceCode" id="cb15"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb15-1" data-line-number="1"><span class="kw">mgeneSim</span>(<span class="dt">genes=</span><span class="kw">c</span>(<span class="st">&quot;835&quot;</span>, <span class="st">&quot;5261&quot;</span>,<span class="st">&quot;241&quot;</span>, <span class="st">&quot;994&quot;</span>),</a>
-<a class="sourceLine" id="cb15-2" data-line-number="2">       <span class="dt">semData=</span>hsGO, <span class="dt">measure=</span><span class="st">&quot;Rel&quot;</span>,<span class="dt">verbose=</span><span class="ot">FALSE</span>)</a></code></pre></div>
-<pre><code>##        835  5261   241   994
-## 835  0.928 0.398 0.276 0.507
-## 5261 0.398 0.939 0.322 0.438
-## 241  0.276 0.322 0.941 0.277
-## 994  0.507 0.438 0.277 0.908</code></pre>
-<p>By default, <code>godata</code> function use <code>ENTREZID</code> as keytype, and the input ID type is <code>ENTREZID</code>. User can use other ID types such as <code>ENSEMBL</code>, <code>UNIPROT</code>, <code>REFSEQ</code>, <code>ACCNUM</code>, <code>SYMBOL</code> <em>et al</em>.</p>
-<p>Here as an example, we use <code>SYMBOL</code> as <code>keytype</code> and calculate semantic similarities among several genes by using their gene symbol as input.</p>
-<div class="sourceCode" id="cb17"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb17-1" data-line-number="1">hsGO2 &lt;-<span class="st"> </span><span class="kw">godata</span>(<span class="st">'org.Hs.eg.db'</span>, <span class="dt">keytype =</span> <span class="st">&quot;SYMBOL&quot;</span>, <span class="dt">ont=</span><span class="st">&quot;MF&quot;</span>, <span class="dt">computeIC=</span><span class="ot">FALSE</span>)</a>
-<a class="sourceLine" id="cb17-2" data-line-number="2">genes &lt;-<span class="st"> </span><span class="kw">c</span>(<span class="st">&quot;CDC45&quot;</span>, <span class="st">&quot;MCM10&quot;</span>, <span class="st">&quot;CDC20&quot;</span>, <span class="st">&quot;NMU&quot;</span>, <span class="st">&quot;MMP1&quot;</span>)</a>
-<a class="sourceLine" id="cb17-3" data-line-number="3"><span class="kw">mgeneSim</span>(genes, <span class="dt">semData=</span>hsGO2, <span class="dt">measure=</span><span class="st">&quot;Wang&quot;</span>, <span class="dt">combine=</span><span class="st">&quot;BMA&quot;</span>, <span class="dt">verbose=</span><span class="ot">FALSE</span>)</a></code></pre></div>
-<pre><code>##       CDC45 MCM10 CDC20   NMU  MMP1
-## CDC45 1.000 0.813 0.594 0.544 0.116
-## MCM10 0.813 1.000 0.647 0.587 0.081
-## CDC20 0.594 0.647 1.000 0.730 0.100
-## NMU   0.544 0.587 0.730 1.000 0.079
-## MMP1  0.116 0.081 0.100 0.079 1.000</code></pre>
-<p>Users can also use <a href="https://bioconductor.org/packages/release/bioc/vignettes/clusterProfiler/inst/doc/clusterProfiler.html#bitr-biological-id-translator"><code>clusterProfiler::bitr</code></a> to translate biological IDs.</p>
-</div>
-<div id="clustersim-and-mclustersim" class="section level2">
-<h2>clusterSim and mclusterSim</h2>
-<p>We also implemented <strong><em>clusterSim</em></strong> for calculating semantic similarity between two gene clusters and <strong><em>mclusterSim</em></strong> for calculating semantic similarities among multiple gene clusters.</p>
-<div class="sourceCode" id="cb19"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb19-1" data-line-number="1">gs1 &lt;-<span class="st"> </span><span class="kw">c</span>(<span class="st">&quot;835&quot;</span>, <span class="st">&quot;5261&quot;</span>,<span class="st">&quot;241&quot;</span>, <span class="st">&quot;994&quot;</span>, <span class="st">&quot;514&quot;</span>, <span class="st">&quot;533&quot;</span>)</a>
-<a class="sourceLine" id="cb19-2" data-line-number="2">gs2 &lt;-<span class="st"> </span><span class="kw">c</span>(<span class="st">&quot;578&quot;</span>,<span class="st">&quot;582&quot;</span>, <span class="st">&quot;400&quot;</span>, <span class="st">&quot;409&quot;</span>, <span class="st">&quot;411&quot;</span>)</a>
-<a class="sourceLine" id="cb19-3" data-line-number="3"><span class="kw">clusterSim</span>(gs1, gs2, <span class="dt">semData=</span>hsGO, <span class="dt">measure=</span><span class="st">&quot;Wang&quot;</span>, <span class="dt">combine=</span><span class="st">&quot;BMA&quot;</span>)</a></code></pre></div>
-<pre><code>## [1] 0.611</code></pre>
-<div class="sourceCode" id="cb21"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb21-1" data-line-number="1"><span class="kw">library</span>(org.Hs.eg.db)</a>
-<a class="sourceLine" id="cb21-2" data-line-number="2">x &lt;-<span class="st"> </span>org.Hs.egGO</a>
-<a class="sourceLine" id="cb21-3" data-line-number="3">hsEG &lt;-<span class="st"> </span><span class="kw">mappedkeys</span>(x)</a>
-<a class="sourceLine" id="cb21-4" data-line-number="4">set.seed &lt;-<span class="st"> </span><span class="dv">123</span></a>
-<a class="sourceLine" id="cb21-5" data-line-number="5">clusters &lt;-<span class="st"> </span><span class="kw">list</span>(<span class="dt">a=</span><span class="kw">sample</span>(hsEG, <span class="dv">20</span>), <span class="dt">b=</span><span class="kw">sample</span>(hsEG, <span class="dv">20</span>), <span class="dt">c=</span><span class="kw">sample</span>(hsEG, <span class="dv">20</span>))</a>
-<a class="sourceLine" id="cb21-6" data-line-number="6"><span class="kw">mclusterSim</span>(clusters, <span class="dt">semData=</span>hsGO, <span class="dt">measure=</span><span class="st">&quot;Wang&quot;</span>, <span class="dt">combine=</span><span class="st">&quot;BMA&quot;</span>)</a></code></pre></div>
-<pre><code>##       a     b     c
-## a 1.000 0.655 0.696
-## b 0.655 1.000 0.640
-## c 0.696 0.640 1.000</code></pre>
-</div>
-</div>
-<div id="applications" class="section level1">
-<h1>Applications</h1>
-<p><a href="https://www.bioconductor.org/packages/GOSemSim">GOSemSim</a> was cited by more than <a href="https://scholar.google.com.hk/scholar?oi=bibs&amp;hl=en&amp;cites=9484177541993722322,17633835198940746971,18126401808149291947">200 papers</a> and had been applied to many research domains, including:</p>
-<ul>
-<li><a href="https://guangchuangyu.github.io/software/GOSemSim/featuredArticles/#diease-or-drug-analysis">Disease or Drug analysis</a></li>
-<li><a href="https://guangchuangyu.github.io/software/GOSemSim/featuredArticles/#geneprotein-functional-analysis">Gene/Protein functional analysis</a></li>
-<li><a href="https://guangchuangyu.github.io/software/GOSemSim/featuredArticles/#protein-protein-interaction">Protein-Protein interaction</a></li>
-<li><a href="https://guangchuangyu.github.io/software/GOSemSim/featuredArticles/#mirna-mrna-interaction">miRNA-mRNA interaction</a></li>
-<li><a href="https://guangchuangyu.github.io/software/GOSemSim/featuredArticles/#srna-regulation">sRNA regulation</a></li>
-<li><a href="https://guangchuangyu.github.io/software/GOSemSim/featuredArticles/#evolution">Evolution</a></li>
-</ul>
-<p>Find out more on <a href="https://guangchuangyu.github.io/software/GOSemSim/featuredArticles/" class="uri">https://guangchuangyu.github.io/software/GOSemSim/featuredArticles/</a>.</p>
-</div>
-<div id="go-enrichment-analysis" class="section level1">
-<h1>GO enrichment analysis</h1>
-<p>GO enrichment analysis can be supported by our package <a href="https://www.bioconductor.org/packages/clusterProfiler">clusterProfiler</a><span class="citation">(Yu et al. 2012)</span>, which supports hypergeometric test and Gene Set Enrichment Analysis (GSEA). Enrichment results across different gene clusters can be compared using <strong><em>compareCluster</em></strong> function.</p>
-</div>
-<div id="disease-ontology-semantic-and-enrichment-analysis" class="section level1">
-<h1>Disease Ontology Semantic and Enrichment analysis</h1>
-<p>Disease Ontology (DO) annotates human genes in the context of disease. DO is an important annotation in translating molecular findings from high-throughput data to clinical relevance. <a href="https://www.bioconductor.org/packages/DOSE">DOSE</a><span class="citation">(Yu et al. 2015)</span> supports semantic similarity computation among DO terms and genes. Enrichment analysis including hypergeometric model and GSEA are also implemented to support discovering disease associations of high-throughput biological data.</p>
-</div>
-<div id="mesh-enrichment-and-semantic-analyses" class="section level1">
-<h1>MeSH enrichment and semantic analyses</h1>
-<p>MeSH (Medical Subject Headings) is the NLM controlled vocabulary used to manually index articles for MEDLINE/PubMed. <a href="https://www.bioconductor.org/packages/meshes">meshes</a> supports enrichment (hypergeometric test and GSEA) and semantic similarity analyses for more than 70 species.</p>
+</ol>
 </div>
 <div id="need-helps" class="section level1">
 <h1>Need helps?</h1>
-<p>If you have questions/issues, please visit <a href="https://guangchuangyu.github.io/software/GOSemSim/">GOSemSim homepage</a> first. Your problems are mostly documented. If you think you found a bug, please follow <a href="https://guangchuangyu.github.io/2016/07/how-to-bug-author/">the guide</a> and provide a reproducible example to be posted on <a href="https://github.com/GuangchuangYu/GOSemSim/issues">github issue tracker</a>. For questions, please post to <a href="https://support.bioconductor.org/">Bioconductor support site</a> and tag your post with <em>GOSemSim</em>.</p>
-<p>For Chinese user, you can follow me on <a href="https://guangchuangyu.github.io/blog_images/biobabble.jpg">WeChat (微信)</a>.</p>
-</div>
-<div id="session-information" class="section level1">
-<h1>Session Information</h1>
-<p>Here is the output of <code>sessionInfo()</code> on the system on which this document was compiled:</p>
-<pre><code>## R version 4.0.2 (2020-06-22)
-## Platform: x86_64-pc-linux-gnu (64-bit)
-## Running under: Ubuntu 18.04.4 LTS
-## 
-## Matrix products: default
-## BLAS:   /home/biocbuild/bbs-3.11-bioc/R/lib/libRblas.so
-## LAPACK: /home/biocbuild/bbs-3.11-bioc/R/lib/libRlapack.so
-## 
-## locale:
-##  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
-##  [3] LC_TIME=en_US.UTF-8        LC_COLLATE=C              
-##  [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
-##  [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
-##  [9] LC_ADDRESS=C               LC_TELEPHONE=C            
-## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       
-## 
-## attached base packages:
-## [1] parallel  stats4    stats     graphics  grDevices utils     datasets 
-## [8] methods   base     
-## 
-## other attached packages:
-## [1] GOSemSim_2.14.2      GO.db_3.11.4         org.Hs.eg.db_3.11.4 
-## [4] AnnotationDbi_1.50.3 IRanges_2.22.2       S4Vectors_0.26.1    
-## [7] Biobase_2.48.0       BiocGenerics_0.34.0 
-## 
-## loaded via a namespace (and not attached):
-##  [1] Rcpp_1.0.5      knitr_1.29      magrittr_1.5    bit_4.0.4      
-##  [5] rlang_0.4.7     stringr_1.4.0   blob_1.2.1      tools_4.0.2    
-##  [9] xfun_0.16       DBI_1.1.0       htmltools_0.5.0 yaml_2.2.1     
-## [13] bit64_4.0.5     digest_0.6.25   prettydoc_0.4.0 vctrs_0.3.4    
-## [17] memoise_1.1.0   evaluate_0.14   RSQLite_2.2.0   rmarkdown_2.3  
-## [21] stringi_1.4.6   compiler_4.0.2  pkgconfig_2.0.3</code></pre>
-</div>
-<div id="references" class="section level1 unnumbered">
-<h1>References</h1>
-<div id="refs" class="references">
-<div id="ref-jiang_semantic_1997">
-<p>Jiang, Jay J., and David W. Conrath. 1997. “Semantic Similarity Based on Corpus Statistics and Lexical Taxonomy.” <em>Proceedings of 10th International Conference on Research in Computational Linguistics</em>. <a href="http://www.citebase.org/abstract?id=oai:arXiv.org:cmp-lg/9709008" class="uri">http://www.citebase.org/abstract?id=oai:arXiv.org:cmp-lg/9709008</a>.</p>
-</div>
-<div id="ref-lin_information-theoretic_1998">
-<p>Lin, Dekang. 1998. “An Information-Theoretic Definition of Similarity.” <em>In Proceedings of the 15th International Conference on Machine Learning</em>, 296—304. <a href="https://doi.org/10.1.1.55.1832" class="uri">https://doi.org/10.1.1.55.1832</a>.</p>
-</div>
-<div id="ref-philip_semantic_1999">
-<p>Philip, Resnik. 1999. “Semantic Similarity in a Taxonomy: An Information-Based Measure and Its Application to Problems of Ambiguity in Natural Language.” <em>Journal of Artificial Intelligence Research</em> 11:95–130.</p>
-</div>
-<div id="ref-schlicker_new_2006">
-<p>Schlicker, Andreas, Francisco S Domingues, Jörg Rahnenführer, and Thomas Lengauer. 2006. “A New Measure for Functional Similarity of Gene Products Based on Gene Ontology.” <em>BMC Bioinformatics</em> 7:302. <a href="https://doi.org/1471-2105-7-302" class="uri">https://doi.org/1471-2105-7-302</a>.</p>
-</div>
-<div id="ref-wang_new_2007">
-<p>Wang, James Z, Zhidian Du, Rapeeporn Payattakool, Philip S Yu, and Chin-Fu Chen. 2007. “A New Method to Measure the Semantic Similarity of Go Terms.” <em>Bioinformatics (Oxford, England)</em> 23 (May):1274–81. <a href="https://doi.org/btm087" class="uri">https://doi.org/btm087</a>.</p>
-</div>
-<div id="ref-yu2010">
-<p>Yu, Guangchuang, Fei Li, Yide Qin, Xiaochen Bo, Yibo Wu, and Shengqi Wang. 2010. “GOSemSim: An R Package for Measuring Semantic Similarity Among Go Terms and Gene Products.” <em>Bioinformatics</em> 26 (april):976–78. <a href="https://doi.org/10.1093/bioinformatics/btq064" class="uri">https://doi.org/10.1093/bioinformatics/btq064</a>.</p>
-</div>
-<div id="ref-yu2012">
-<p>Yu, Guangchuang, Li-Gen Wang, Yanyan Han, and Qing-Yu He. 2012. “clusterProfiler: an R Package for Comparing Biological Themes Among Gene Clusters.” <em>OMICS: A Journal of Integrative Biology</em> 16 (5):284–87. <a href="https://doi.org/10.1089/omi.2011.0118" class="uri">https://doi.org/10.1089/omi.2011.0118</a>.</p>
-</div>
-<div id="ref-yu_dose_2015">
-<p>Yu, Guangchuang, Li-Gen Wang, Guang-Rong Yan, and Qing-Yu He. 2015. “DOSE: An R/Bioconductor Package for Disease Ontology Semantic and Enrichment Analysis.” <em>Bioinformatics</em> 31 (4):608–9. <a href="https://doi.org/10.1093/bioinformatics/btu684" class="uri">https://doi.org/10.1093/bioinformatics/btu684</a>.</p>
-</div>
-</div>
+<p>For questions, please post to <a href="https://support.bioconductor.org/">Bioconductor support site</a> and tag your post with <em>GOSemSim</em>.</p>
 </div>
 </section>
 
diff -Nru r-bioc-gosemsim-2.14.2/inst/doc/GOSemSim.R r-bioc-gosemsim-2.16.1/inst/doc/GOSemSim.R
--- r-bioc-gosemsim-2.14.2/inst/doc/GOSemSim.R	2020-09-04 21:48:50.000000000 +0000
+++ r-bioc-gosemsim-2.16.1/inst/doc/GOSemSim.R	2020-10-30 00:15:30.000000000 +0000
@@ -4,57 +4,7 @@
                       message = FALSE)
 
 ## ----echo=FALSE, results="hide", message=FALSE--------------------------------
-library(org.Hs.eg.db)
-library(GO.db)
-library(GOSemSim)
-
 Biocpkg <- function (pkg) {
     sprintf("[%s](http://bioconductor.org/packages/%s)", pkg, pkg)
 }
 
-## ----eval=FALSE---------------------------------------------------------------
-#  library(AnnotationHub)
-#  hub <- AnnotationHub()
-#  q <- query(hub, "Cricetulus")
-#  id <- q$ah_id[length(q)]
-#  Cgriseus <- hub[[id]]
-
-## -----------------------------------------------------------------------------
-library(GOSemSim)
-hsGO <- godata('org.Hs.eg.db', ont="MF")
-
-## -----------------------------------------------------------------------------
-goSim("GO:0004022", "GO:0005515", semData=hsGO, measure="Jiang")
-goSim("GO:0004022", "GO:0005515", semData=hsGO, measure="Wang")
-go1 = c("GO:0004022","GO:0004024","GO:0004174")
-go2 = c("GO:0009055","GO:0005515")
-mgoSim(go1, go2, semData=hsGO, measure="Wang", combine=NULL)
-mgoSim(go1, go2, semData=hsGO, measure="Wang", combine="BMA")
-
-## -----------------------------------------------------------------------------
-geneSim("241", "251", semData=hsGO, measure="Wang", combine="BMA")
-mgeneSim(genes=c("835", "5261","241", "994"),
-         semData=hsGO, measure="Wang",verbose=FALSE)
-mgeneSim(genes=c("835", "5261","241", "994"),
-       semData=hsGO, measure="Rel",verbose=FALSE)
-
-## -----------------------------------------------------------------------------
-hsGO2 <- godata('org.Hs.eg.db', keytype = "SYMBOL", ont="MF", computeIC=FALSE)
-genes <- c("CDC45", "MCM10", "CDC20", "NMU", "MMP1")
-mgeneSim(genes, semData=hsGO2, measure="Wang", combine="BMA", verbose=FALSE)
-
-## -----------------------------------------------------------------------------
-gs1 <- c("835", "5261","241", "994", "514", "533")
-gs2 <- c("578","582", "400", "409", "411")
-clusterSim(gs1, gs2, semData=hsGO, measure="Wang", combine="BMA")
-
-library(org.Hs.eg.db)
-x <- org.Hs.egGO
-hsEG <- mappedkeys(x)
-set.seed <- 123
-clusters <- list(a=sample(hsEG, 20), b=sample(hsEG, 20), c=sample(hsEG, 20))
-mclusterSim(clusters, semData=hsGO, measure="Wang", combine="BMA")
-
-## ----echo=FALSE---------------------------------------------------------------
-sessionInfo()
-
diff -Nru r-bioc-gosemsim-2.14.2/inst/doc/GOSemSim.Rmd r-bioc-gosemsim-2.16.1/inst/doc/GOSemSim.Rmd
--- r-bioc-gosemsim-2.14.2/inst/doc/GOSemSim.Rmd	2020-04-27 20:23:20.000000000 +0000
+++ r-bioc-gosemsim-2.16.1/inst/doc/GOSemSim.Rmd	2020-10-27 17:25:39.000000000 +0000
@@ -1,12 +1,10 @@
 ---
 title: "GO Semantic Similarity Analysis"
-author: "\\
-
-	Guangchuang Yu\\
-
-        School of Basic Medical Sciences, Southern Medical University"
+author: 
+- name: Guangchuang Yu
+  email: guangchuangyu@gmail.com
+  affiliation: Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University
 date: "`r Sys.Date()`"
-bibliography: GOSemSim.bib
 output:
   prettydoc::html_pretty:
     toc: true
@@ -15,7 +13,7 @@
   pdf_document:
     toc: true
 vignette: >
-  % \VignetteIndexEntry{An introduction to GOSemSim}
+  % \VignetteIndexEntry{GOSemSim}
   % \VignetteEngine{knitr::rmarkdown}
   % \usepackage[utf8]{inputenc}
   %\VignetteEncoding{UTF-8}
@@ -28,314 +26,28 @@
 ```
 
 ```{r echo=FALSE, results="hide", message=FALSE}
-library(org.Hs.eg.db)
-library(GO.db)
-library(GOSemSim)
-
 Biocpkg <- function (pkg) {
     sprintf("[%s](http://bioconductor.org/packages/%s)", pkg, pkg)
 }
 ```
 
-# Citation
-
-
-If you use `r Biocpkg('GOSemSim')` in published research, please cite:
-
-+ **Yu G**. [Gene Ontology Semantic Similarity Analysis Using GOSemSim](http://dx.doi.org/10.1007/978-1-0716-0301-7_11). In: Kidder B. (eds) Stem Cell Transcriptional Networks. **_Methods in Molecular Biology_**, 2020, 2117:207-215. Humana, New York, NY. 
-+ **Yu G**^#^, Li F^#^, Qin Y, Bo X^\*^, Wu Y and Wang S^\*^. [GOSemSim: an R package for measuring semantic similarity among GO terms and gene products](http://dx.doi.org/10.1093/bioinformatics/btq064). **_Bioinformatics_**. 2010, 26(7):976-978. 
-
-
-
-# Introduction
-
-Functional similarity of gene products can be estimated by controlled
-biological vocabularies, such as Gene Ontology (GO) and Disease Ontology (DO). GO comprises of
-three orthogonal ontologies, i.e. molecular function (MF), biological
-process (BP), and cellular component (CC).
-
-
-Four methods including Resnik[@philip_semantic_1999], Jiang[@jiang_semantic_1997],
-Lin[@lin_information-theoretic_1998] and Schlicker[@schlicker_new_2006] have been presented to determine the semantic similarity
-of two GO terms based on the annotation statistics of their common
-ancestor terms. Wang[@wang_new_2007]
-proposed a method to measure the similarity based on the graph structure of
-GO. Each of these methods has its own advantages and
-weaknesses.
-
-[GOSemSim](https://www.bioconductor.org/packages/GOSemSim) package[@yu2010] is developed to compute
-semantic similarity among GO terms, sets of GO terms, gene products,
-and gene clusters, providing five methods mentioned above. We have
-developed another package, [DOSE](https://www.bioconductor.org/packages/DOSE)[@yu_dose_2015], for measuring semantic
-similarity among Disease Ontology (DO) terms and gene products at disease perspective.
-
-
-# Semantic Similarity Measurement Based on GO
-
-## Information content-based methods
-
-Four methods proposed by Resnik[@philip_semantic_1999],
-Jiang[@jiang_semantic_1997], Lin[@lin_information-theoretic_1998]
-and Schlicker[@schlicker_new_2006] are information content (IC) based, which
-depend on the frequencies of two GO terms involved and that of their
-closest common ancestor term in a specific corpus of GO
-annotations. The information content of a GO term is computed by the
-negative log probability of the term occurring in GO corpus. A rarely used term contains a
-greater amount of information.
-
-The frequency of a term t is defined as:
-$p(t) = \frac{n_{t'}}{N} | t' \in \left\{t, \; children\: of\: t \right\}$
-
-where $n_{t'}$ is the number of term $t'$, and $N$ is the total number of terms in GO corpus.
-
-Thus the information content is defined as:
-$IC(t) = -\log(p(t))$
-
-As GO allow multiple parents for each concept, two terms can share
-parents by multiple paths. IC-based methods calculate similarity of two GO terms based on the information content of their closest common ancestor term, which was also called most informative common ancestor (MICA).
-
-### Resnik method
-
-The Resnik method is defined as:
-$sim_{Resnik}(t_1,t_2) = IC(MICA)$
-
-### Lin method
-
-The Lin method is defined as:
-$sim_{Lin}(t_1,t_2) = \frac{2IC(MICA)}{IC(t_1)+IC(t_2)}$
-
-### Rel method
-
-The Relevance method, which was proposed by Schlicker, combine Resnik's and Lin's method
-and is defined as:
-$sim_{Rel}(t_1,t_2) = \frac{2IC(MICA)(1-p(MICA))}{IC(t_1)+IC(t_2)}$
-
-### Jiang method
-
-The Jiang and Conrath's method is defined as:
-$sim_{Jiang}(t_1,t_2) = 1-\min(1, IC(t_1) + IC(t_2) - 2IC(MICA))$
-
-
-## Graph-based method
-Graph-based methods using the topology of GO graph structure to
-compute semantic similarity. Formally, a GO term A can be represented
-as $DAG_{A}=(A,T_{A},E_{A})$ where $T_{A}$ is the set of GO terms in
-$DAG_{A}$, including term A and all of its ancestor terms in the GO
-graph, and $E_{A}$ is the set of edges connecting the GO terms in
-$DAG_{A}$.
-
-### Wang method
-To encode the semantic of a GO term in a measurable format to enable a quantitative comparison, Wang[@wang_new_2007] firstly defined the semantic value of term A as the aggregate contribution of all terms in $DAG_{A}$ to the semantics of term A, terms closer to term A in $DAG_{A}$ contribute more to its semantics. Thus, defined the contribution of a GO term $t$ to the semantic of GO term $A$ as the S-value of GO term $t$ related to term $A$.
-For any of term $t$ in $DAG_{A}$, its S-value related to term $A$, $S_{A}(\textit{t})$ is defined as:
-
-
-$\left\{\begin{array}{l} S_{A}(A)=1 \\ S_{A}(\textit{t})=\max\{w_{e} \times S_{A}(\textit{t}') | \textit{t}' \in children \: of(\textit{t}) \} \; if \: \textit{t} \ne A \end{array} \right.$
-
-where $w_{e}$ is the semantic contribution factor for edge $e \in E_{A}$ linking term $t$ with its child term $t'$.
-Term $A$ contributes to its own is defined as 1. After obtaining the S-values for all terms in $DAG_{A}$,
-the semantic value of DO term A, $SV(A)$, is calculated as:
-
-$SV(A)=\displaystyle\sum_{t \in T_{A}} S_{A}(t)$
-
-Thus given two GO terms A and B, the semantic similarity between these two terms is defined as:
-
-$sim_{Wang}(A, B) = \frac{\displaystyle\sum_{t \in T_{A} \cap T_{B}}{S_{A}(t) + S_{B}(t)}}{SV(A) + SV(B)}$
-
-where $S_{A}(\textit{t})$ is the S-value of GO term $t$ related to term $A$
-and $S_{B}(\textit{t})$ is the S-value of GO term $t$ related to term $B$.
-
-This method proposed by Wang[@wang_new_2007] determines the semantic
-similarity of two GO terms based on both the locations of these terms
-in the GO graph and their relations with their ancestor terms.
-
-## Supported organisms
-
-For IC-based methods, information of GO term is species specific. We
-need to calculate `IC` for all GO terms of a species before we measure
-semantic
-similarity. [GOSemSim](https://www.bioconductor.org/packages/GOSemSim)
-support all organisms that have an `OrgDb` object available.
-
-Bioconductor have already provided `OrgDb` for about 20 species, see <http://bioconductor.org/packages/release/BiocViews.html#___OrgDb>.
-
-We can build query `OrgDb` online via [AnnotationHub](https://www.bioconductor.org/packages/AnnotationHub). For example:
-
-```{r eval=FALSE}
-library(AnnotationHub)
-hub <- AnnotationHub()
-q <- query(hub, "Cricetulus")
-id <- q$ah_id[length(q)]
-Cgriseus <- hub[[id]]
-```
-
-
-If organism is not supported by [AnnotationHub](https://www.bioconductor.org/packages/AnnotationHub), user can use [AnnotationForge](https://www.bioconductor.org/packages/AnnotationForge) to build `OrgDb`.
-
-Once we have `OrgDb`, we can build annotation data needed by [GOSemSim](https://www.bioconductor.org/packages/GOSemSim) via `godata` function.
-
-```{r}
-library(GOSemSim)
-hsGO <- godata('org.Hs.eg.db', ont="MF")
-```
-
-User can set `computeIC=FALSE` if they only want to use Wang's method.
-
-
-## goSim and mgoSim function
+# Vignette
 
-In [GOSemSim](https://www.bioconductor.org/packages/GOSemSim), we implemented all these IC-based and graph-based methods. __*goSim*__ function calculates semantic similarity between two GO terms, while __*mgoSim*__ function calculates semantic similarity between two sets of GO terms.
+Please go to <https://yulab-smu.top/biomedical-knowledge-mining-book/> for the full vignette.
 
 
-```{r}
-goSim("GO:0004022", "GO:0005515", semData=hsGO, measure="Jiang")
-goSim("GO:0004022", "GO:0005515", semData=hsGO, measure="Wang")
-go1 = c("GO:0004022","GO:0004024","GO:0004174")
-go2 = c("GO:0009055","GO:0005515")
-mgoSim(go1, go2, semData=hsGO, measure="Wang", combine=NULL)
-mgoSim(go1, go2, semData=hsGO, measure="Wang", combine="BMA")
-```
-
-# Gene Semantic Similarity Measurement
-
-On the basis of semantic similarity between GO terms, [GOSemSim](https://www.bioconductor.org/packages/GOSemSim) can
-also compute semantic similarity among sets of GO terms, gene products, and gene clusters.
-
-Suppose we have gene $g_1$ annotated by GO terms sets $GO_{1}=\{go_{11},go_{12} \cdots go_{1m}\}$
-and $g_2$ annotated by $GO_{2}=\{go_{21},go_{22} \cdots go_{2n}\}$, [GOSemSim](https://www.bioconductor.org/packages/GOSemSim) implemented four methods which called __*max*__, __*avg*__, __*rcmax*__, and __*BMA*__ to combine
-semantic similarity scores of multiple GO terms. The similarities
-among gene products and gene clusters which annotated by multiple GO
-terms were also calculated by the same combine methods mentioned
-above.
-
-
-## Combine methods
-
-### max
-
-The __*max*__ method calculates the maximum semantic similarity score over all pairs of GO terms between these two GO term sets.
-
-
-$sim_{max}(g_1, g_2) = \displaystyle\max_{1 \le i \le m, 1 \le j \le n} sim(go_{1i}, go_{2j})$
-
-### avg
-
-The __*avg*__ calculates the average semantic similarity score over all pairs of GO terms.
-
-
-$sim_{avg}(g_1, g_2) = \frac{\displaystyle\sum_{i=1}^m\sum_{j=1}^nsim(go_{1i}, go_{2j})}{m \times n}$
-
-### rcmax
-
-Similarities among two sets of GO terms form a matrix, the __*rcmax*__ method uses the maximum of _RowScore_ and _ColumnScore_, where _RowScore_ (or _ColumnScore_) is the average of maximum similarity on each row (or column).
-
-
-$sim_{rcmax}(g_1, g_2) = \max(\frac{\displaystyle\sum_{i=1}^m \max_{1 \le j \le n} sim(go_{1i}, go_{2j})}{m},\frac{\displaystyle\sum_{j=1}^n \max_{1 \le i \le m} sim(go_{1i},go_{2j})}{n})$
-
-### BMA
-
-The __*BMA*__ method, used the **B**est-**M**atch **A**verage strategy, calculates the average of all maximum similarities on each row and column, and is defined as:
-
-
-$sim_{BMA}(g_1, g_2) = \frac{\displaystyle\sum_{1=i}^m \max_{1 \le j \le n}sim(go_{1i}, go_{2j}) + \displaystyle\sum_{1=j}^n \max_{1 \le i \le m}sim(go_{1i}, go_{2j})} {m+n}$
-
-
-## geneSim and mgeneSim
-
-In [GOSemSim](https://www.bioconductor.org/packages/GOSemSim), we implemented __*geneSim*__ to calculate semantic similarity between two gene products, and __*mgeneSim*__ to calculate semantic similarity among multiple gene products.
-
-```{r}
-geneSim("241", "251", semData=hsGO, measure="Wang", combine="BMA")
-mgeneSim(genes=c("835", "5261","241", "994"),
-         semData=hsGO, measure="Wang",verbose=FALSE)
-mgeneSim(genes=c("835", "5261","241", "994"),
-       semData=hsGO, measure="Rel",verbose=FALSE)
-```
-
-By default, `godata` function use `ENTREZID` as keytype, and the input ID type is `ENTREZID`. User can use other ID types such as `ENSEMBL`, `UNIPROT`, `REFSEQ`, `ACCNUM`, `SYMBOL` _et al_.
-
-Here as an example, we use `SYMBOL` as `keytype` and calculate semantic similarities among several genes by using their gene symbol as input.
-
-```{r}
-hsGO2 <- godata('org.Hs.eg.db', keytype = "SYMBOL", ont="MF", computeIC=FALSE)
-genes <- c("CDC45", "MCM10", "CDC20", "NMU", "MMP1")
-mgeneSim(genes, semData=hsGO2, measure="Wang", combine="BMA", verbose=FALSE)
-```
-
-Users can also use [`clusterProfiler::bitr`](https://bioconductor.org/packages/release/bioc/vignettes/clusterProfiler/inst/doc/clusterProfiler.html#bitr-biological-id-translator) to translate biological IDs.
-
-
-## clusterSim and mclusterSim
-
-We also implemented __*clusterSim*__ for calculating semantic similarity between two gene clusters
-and __*mclusterSim*__ for calculating semantic similarities among multiple gene clusters.
-
-```{r}
-gs1 <- c("835", "5261","241", "994", "514", "533")
-gs2 <- c("578","582", "400", "409", "411")
-clusterSim(gs1, gs2, semData=hsGO, measure="Wang", combine="BMA")
-
-library(org.Hs.eg.db)
-x <- org.Hs.egGO
-hsEG <- mappedkeys(x)
-set.seed <- 123
-clusters <- list(a=sample(hsEG, 20), b=sample(hsEG, 20), c=sample(hsEG, 20))
-mclusterSim(clusters, semData=hsGO, measure="Wang", combine="BMA")
-```
-
-# Applications
-
-[GOSemSim](https://www.bioconductor.org/packages/GOSemSim) was cited by more than [200 papers](https://scholar.google.com.hk/scholar?oi=bibs&hl=en&cites=9484177541993722322,17633835198940746971,18126401808149291947) and had been applied to many research domains, including:
-
-+ [Disease or Drug analysis](https://guangchuangyu.github.io/software/GOSemSim/featuredArticles/#diease-or-drug-analysis)
-+ [Gene/Protein functional analysis](https://guangchuangyu.github.io/software/GOSemSim/featuredArticles/#geneprotein-functional-analysis)
-+ [Protein-Protein interaction](https://guangchuangyu.github.io/software/GOSemSim/featuredArticles/#protein-protein-interaction)
-+ [miRNA-mRNA interaction](https://guangchuangyu.github.io/software/GOSemSim/featuredArticles/#mirna-mrna-interaction)
-+ [sRNA regulation](https://guangchuangyu.github.io/software/GOSemSim/featuredArticles/#srna-regulation)
-+ [Evolution](https://guangchuangyu.github.io/software/GOSemSim/featuredArticles/#evolution)
-
-Find out more on <https://guangchuangyu.github.io/software/GOSemSim/featuredArticles/>.
-
-
-# GO enrichment analysis
-
-GO enrichment analysis can be supported by our package [clusterProfiler](https://www.bioconductor.org/packages/clusterProfiler)[@yu2012], which supports hypergeometric test and Gene Set Enrichment Analysis (GSEA). Enrichment results across different gene clusters can be compared using __*compareCluster*__ function.
-
-# Disease Ontology Semantic and Enrichment analysis
+# Citation
 
-Disease Ontology (DO) annotates human genes in the context of disease. DO is an important annotation in translating molecular findings from high-throughput data to clinical relevance.
-[DOSE](https://www.bioconductor.org/packages/DOSE)[@yu_dose_2015] supports semantic similarity computation among DO terms and genes.
-Enrichment analysis including hypergeometric model and GSEA are also implemented to support discovering disease associations of high-throughput biological data.
 
-# MeSH enrichment and semantic analyses
+If you use `r Biocpkg('GOSemSim')` in published research, please cite the most appropriate paper(s) from this list:
 
-MeSH (Medical Subject Headings) is the NLM controlled vocabulary used to manually index articles for MEDLINE/PubMed. [meshes](https://www.bioconductor.org/packages/meshes) supports enrichment (hypergeometric test and GSEA) and semantic similarity analyses for more than 70 species.
+1. **Yu G**. [Gene Ontology Semantic Similarity Analysis Using GOSemSim](http://dx.doi.org/10.1007/978-1-0716-0301-7_11). In: Kidder B. (eds) Stem Cell Transcriptional Networks. **_Methods in Molecular Biology_**, 2020, 2117:207-215. Humana, New York, NY. 
+2. **Yu G**^#^, Li F^#^, Qin Y, Bo X^\*^, Wu Y and Wang S^\*^. [GOSemSim: an R package for measuring semantic similarity among GO terms and gene products](http://dx.doi.org/10.1093/bioinformatics/btq064). **_Bioinformatics_**. 2010, 26(7):976-978. 
 
 
 
 # Need helps?
 
 
-If you have questions/issues, please visit
-[GOSemSim homepage](https://guangchuangyu.github.io/software/GOSemSim/) first.
-Your problems are mostly documented. If you think you found a bug, please follow
-[the guide](https://guangchuangyu.github.io/2016/07/how-to-bug-author/) and
-provide a reproducible example to be posted
-on
-[github issue tracker](https://github.com/GuangchuangYu/GOSemSim/issues).
-For questions, please post
-to [Bioconductor support site](https://support.bioconductor.org/) and tag your
-post with *GOSemSim*.
-
-
-For Chinese user, you can follow me on [WeChat (微信)](https://guangchuangyu.github.io/blog_images/biobabble.jpg).
-
-# Session Information
-
-Here is the output of `sessionInfo()` on the system on which this document was compiled:
-
-```{r echo=FALSE}
-sessionInfo()
-```
-
-# References
+For questions, please post to [Bioconductor support site](https://support.bioconductor.org/) and tag your post with *GOSemSim*.
 
diff -Nru r-bioc-gosemsim-2.14.2/NEWS.md r-bioc-gosemsim-2.16.1/NEWS.md
--- r-bioc-gosemsim-2.14.2/NEWS.md	2020-04-27 20:23:20.000000000 +0000
+++ r-bioc-gosemsim-2.16.1/NEWS.md	2020-10-27 17:25:39.000000000 +0000
@@ -1,3 +1,18 @@
+# GOSemSim 2.15.2
+
++ new site, <https://yulab-smu.top/biomedical-knowledge-mining-book/> for documentation (2020-09-04, Fri)
++ update vignette
++ update `data/gotbl`
+
+# GOSemSim 2.15.1
+
++ bug fixed of IC method when input IDs contain invalid terms. (2020-07-25, Sat)
+
+# GOSemSim 2.14.0
+
++ Bioconductor 3.11 release
+
+
 # GOSemSim 2.13.1
 
 + add new citation (2020-03-19, Thu)
diff -Nru r-bioc-gosemsim-2.14.2/R/ICMethods.R r-bioc-gosemsim-2.16.1/R/ICMethods.R
--- r-bioc-gosemsim-2.14.2/R/ICMethods.R	2020-07-25 18:51:39.000000000 +0000
+++ r-bioc-gosemsim-2.16.1/R/ICMethods.R	2020-10-29 18:48:34.000000000 +0000
@@ -29,15 +29,18 @@
         ##     return(NA)
         ## }
         ## .anc <- AnnotationDbi::as.list(.anc)
-        .anc <- AnnotationDbi::as.list(getAncestors(ont))
-        allid=union(ID1, ID2)
 
-        invalid_ids <- c(ID1[!ID1 %in% names(.anc)], ID2[!ID2 %in% names(.anc)])
-        if (length(invalid_ids) > 0) {
-            message("The following IDs are not valid and will be removed:", paste(invalid_ids, collapse=","))
-            allid <- allid[!allid %in% invalid_ids]
-        }
+        ## if some IDs are not valid, the above code will leading to return NA directly.
+        .anc <- AnnotationDbi::as.list(getAncestors(ont))
+        allid <- union(ID1, ID2)
         .anc <- .anc[allid]
+        .anc <- .anc[!vapply(.anc, is.empty, logical(1))]
+
+        ## invalid_ids <- c(ID1[!ID1 %in% names(.anc)], ID2[!ID2 %in% names(.anc)])
+        ## if (length(invalid_ids) > 0) {
+        ##     message("The following IDs are not valid and will be removed:", paste(invalid_ids, collapse=","))
+        ##     # allid <- allid[!allid %in% invalid_ids]
+        ## }
     } else {
         mesh_getAnc <- eval(parse(text="meshes:::getAncestors"))
         .anc <- lapply(union(ID1, ID2), mesh_getAnc)
@@ -48,6 +51,12 @@
                  method, ont ) )
 }
 
+is.empty <- function(x) {
+    if (is.null(x)) return(TRUE)
+    if (all(is.na(x))) return(TRUE)
+    return(FALSE)
+}
+
 
 ## infoContentMethod <- function(ID1,
 ##                               ID2,
diff -Nru r-bioc-gosemsim-2.14.2/R/utilities.R r-bioc-gosemsim-2.16.1/R/utilities.R
--- r-bioc-gosemsim-2.14.2/R/utilities.R	2020-04-27 20:23:20.000000000 +0000
+++ r-bioc-gosemsim-2.16.1/R/utilities.R	2020-10-27 17:25:39.000000000 +0000
@@ -74,7 +74,8 @@
     
     ptb <- lapply(c("BP", "MF", "CC"), function(ont) {
         id <- with(gtb, go_id[Ontology == ont])
-        pid <- mget(id, getParents(ont))
+        parentMap <- getParents(ont)
+        pid <- mget(id, parentMap)
         
         n <- sapply(pid, length)
         cid <- rep(names(pid), times=n)
@@ -88,6 +89,6 @@
     ptb <- do.call('rbind', ptb)
 
     gotbl <- merge(gtb, ptb, by.x="go_id", by.y="id")
-    save(gotbl, file="gotbl", compress="xz")
+    save(gotbl, file="gotbl.rda", compress="xz")
     invisible(gotbl)
 }
diff -Nru r-bioc-gosemsim-2.14.2/src/ICmethod.cpp r-bioc-gosemsim-2.16.1/src/ICmethod.cpp
--- r-bioc-gosemsim-2.14.2/src/ICmethod.cpp	2020-04-27 20:23:20.000000000 +0000
+++ r-bioc-gosemsim-2.16.1/src/ICmethod.cpp	2020-10-29 18:48:34.000000000 +0000
@@ -95,10 +95,18 @@
   // set root node IC to 0
   if(ont_ == "DO") {
     normIcMap["DOID:4"] = 0;
+  } else if(ont_ == "BP") {
+    normIcMap["GO:0008150"] = 0;
+  } else if(ont_ == "CC") {
+    normIcMap["GO:0005575"] = 0;
+  } else if(ont_ == "MF") {
+    normIcMap["GO:0003674"] = 0;
   } else {
-    normIcMap["all"] = 0;
+    // seems to remove in GO.db 3.12.0
+    normIcMap["all"] = 0; 
   }
 
+
   // convert anc_ into map of sets
   typedef std::map<term_id_t, term_set_t> anc_map_t;
   anc_map_t ancMap;
diff -Nru r-bioc-gosemsim-2.14.2/vignettes/GOSemSim.bib r-bioc-gosemsim-2.16.1/vignettes/GOSemSim.bib
--- r-bioc-gosemsim-2.14.2/vignettes/GOSemSim.bib	2020-04-27 20:23:20.000000000 +0000
+++ r-bioc-gosemsim-2.16.1/vignettes/GOSemSim.bib	1970-01-01 00:00:00.000000000 +0000
@@ -1,153 +0,0 @@
-
-@article{yu_dose_2015,
-	title = {{DOSE}: an R/Bioconductor package for disease ontology semantic and enrichment analysis},
-	volume = {31},
-	issn = {1367-4803, 1460-2059},
-	url = {http://bioinformatics.oxfordjournals.org.eproxy2.lib.hku.hk/content/31/4/608},
-	doi = {10.1093/bioinformatics/btu684},
-	shorttitle = {{DOSE}},
-	abstract = {Summary: Disease ontology ({DO}) annotates human genes in the context of disease. {DO} is important annotation in translating molecular findings from high-throughput data to clinical relevance. {DOSE} is an R package providing semantic similarity computations among {DO} terms and genes which allows biologists to explore the similarities of diseases and of gene functions in disease perspective. Enrichment analyses including hypergeometric model and gene set enrichment analysis are also implemented to support discovering disease associations of high-throughput biological data. This allows biologists to verify disease relevance in a biological experiment and identify unexpected disease associations. Comparison among gene clusters is also supported.
-Availability and implementation: {DOSE} is released under Artistic-2.0 License. The source code and documents are freely available through Bioconductor (http://www.bioconductor.org/packages/release/bioc/html/{DOSE}.html).
-Supplementary information: Supplementary data are available at Bioinformatics online.
-Contact: gcyu@connect.hku.hk or tqyhe@jnu.edu.cn},
-	pages = {608--609},
-	number = {4},
-	journaltitle = {Bioinformatics},
-	shortjournal = {Bioinformatics},
-	author = {Yu, Guangchuang and Wang, Li-Gen and Yan, Guang-Rong and He, Qing-Yu},
-	urldate = {2015-02-13},
-	date = {2015-02-15},
-	langid = {english}
-}
-
-@article{yu2012,
-	title = {{clusterProfiler:} an R Package for Comparing Biological Themes Among Gene Clusters},
-	volume = {16},
-	issn = {1536-2310, 1557-8100},
-	shorttitle = {{clusterProfiler}},
-	url = {http://online.liebertpub.com/doi/abs/10.1089/omi.2011.0118},
-	doi = {10.1089/omi.2011.0118},
-	number = {5},
-	urldate = {2012-05-05},
-	journal = {{OMICS:} A Journal of Integrative Biology},
-	author = {Yu, Guangchuang and Wang, Li-Gen and Han, Yanyan and He, Qing-Yu},
-	month = may,
-	year = {2012},
-	pages = {284--287},
-	file = {2012-OMICS-clusterProfiler.pdf:/Volumes/YGC/MyZotero/storage/3ZEKB39T/2012-OMICS-clusterProfiler.pdf:application/pdf;clusterProfiler: an R Package for Comparing Biological Themes Among Gene Clusters | Abstract:/Volumes/YGC/MyZotero/storage/WPTJGAT7/omi.2011.html:text/html}
-}
-
-
-@Article{yu2011,
-	title = {Functional similarity analysis of human virus-encoded {miRNAs}},
-	volume = {1},
-	issn = {2043-9113},
-	url = {http://www.jclinbioinformatics.com/content/1/1/15},
-	doi = {10.1186/2043-9113-1-15},
-	number = {1},
-	journal = {Journal of Clinical Bioinformatics},
-	author = {Yu, Guangchuang and He, {Qing-Yu}},
-	month = may,
-	year = {2011},
-	pages = {15}
-}
-
-@Article{yu_new_2011,
-	title = {A new method for measuring functional similarity of microRNAs},
-	volume = {1},
-	issn = {2182-0287},
-	url = {http://www.jiomics.com/index.php/jio/article/view/21},
-	doi = {10.5584/jiomics.v1i1.21},
-	number = {1},
-	journal = {Journal of Integrated {OMICS}},
-	author = {Yu, Guangchuang and Xiao, {Chuan-Le} and Bo, Xiaochen and Lu, {Chun-Hua} and Qin, Yide and Zhan, Sheng and He, {Qing-Yu}},
-	month = feb,
-	year = {2011},
-	keywords = {Clustering, Gene, {microRNA;}, Ontology;, Semantic, Similarity;},
-	pages = {49--54}
-}
-
-@Article{yu2010,
-	title = {GOSemSim: an R package for measuring semantic similarity among GO terms and gene products},
-	volume = {26},
-	issn = {1367-4803},
-	url = {http://bioinformatics.oxfordjournals.org/cgi/content/abstract/26/7/976},
-	doi = {10.1093/bioinformatics/btq064},
-	journal = {Bioinformatics},
-	author = {Guangchuang Yu and Fei Li and Yide Qin and Xiaochen Bo and Yibo Wu and Shengqi Wang},
-	month = April,
-	year = {2010},
-	note = {PMID: 20179076},
-	keywords = {Gene Ontology, Semantic Similarity, Bioconductor},
-	pages = {976-978}
-}
-
-@Article{wang_new_2007,
-	title = {A new method to measure the semantic similarity of GO terms},
-	volume = {23},
-	issn = {1460-2059},
-	url = {http://www.ncbi.nlm.nih.gov/pubmed/17344234},
-	doi = {btm087},
-	journal = {Bioinformatics (Oxford, England)},
-	author = {James Z Wang and Zhidian Du and Rapeeporn Payattakool and Philip S Yu and Chin-Fu Chen},
-	month = may,
-	year = {2007},
-	note = {PMID: 17344234},
-	keywords = {Algorithms,Computational Biology,Databases, Genetic,Humans,Semantics,Terminology as Topic,Vocabulary, Controlled},
-	pages = {1274-81}
-}
-
-@Article{lord_semantic_2003,
-	title = {Semantic similarity measures as tools for exploring the gene ontology},
-	issn = {1793-5091},
-	url = {http://www.ncbi.nlm.nih.gov/pubmed/12603061},
-	doi = {12603061},
-	journal = {Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing},
-	author = {P W Lord and R D Stevens and A Brass and C A Goble},
-	year = {2003},
-	note = {PMID: 12603061},
-	keywords = {Classification,Computational Biology,Databases, Protein,Genomics,Humans,Proteomics,Sequence Alignment},
-	pages = {601-12}
-}
-
-@Article{jiang_semantic_1997,
-	title = {Semantic Similarity Based on Corpus Statistics and Lexical Taxonomy},
-	url = {http://www.citebase.org/abstract?id=oai:arXiv.org:cmp-lg/9709008},
-	journal={Proceedings of 10th International Conference on Research In Computational Linguistics},
-	author = {Jay J. Jiang and David W. Conrath},
-	year = {1997}
-}
-
-@Article{philip_semantic_1999,
-	title = {Semantic Similarity in a Taxonomy: An {Information-Based} Measure and its Application to Problems of Ambiguity in Natural Language},
-	volume = {11},
-	url = {},
-	journal = {Journal of Artificial Intelligence Research},
-	author = {Resnik Philip},
-	year = {1999},
-	pages = {95--130}
-}
-
-@Article{lin_information-theoretic_1998,
-	title = {An {Information-Theoretic} Definition of Similarity},
-	url = {http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.55.1832},
-	doi = {10.1.1.55.1832},
-	journal = {In Proceedings of the 15th International Conference on Machine Learning},
-	author = {Dekang Lin},
-	year = {1998},
-	pages = {296---304}
-}
-
-@Article{schlicker_new_2006,
-	title = {A new measure for functional similarity of gene products based on Gene Ontology},
-	volume = {7},
-	issn = {1471-2105},
-	doi = {1471-2105-7-302},
-	journal = {BMC Bioinformatics},
-	author = {Andreas Schlicker and Francisco S Domingues and Jörg Rahnenführer and Thomas Lengauer},
-	year = {2006},
-	note = {PMID: 16776819},
-	keywords = {{Algorithms,Artificial} {Intelligence,Databases,} {Protein,Genes,Information} Storage and {Retrieval,Multigene} {Family,Natural} Language {Processing,Proteins,Structure-Activity} {Relationship,User-Computer} {Interface,Vocabulary,} Controlled},
-	pages = {302}
-}
-
diff -Nru r-bioc-gosemsim-2.14.2/vignettes/GOSemSim.Rmd r-bioc-gosemsim-2.16.1/vignettes/GOSemSim.Rmd
--- r-bioc-gosemsim-2.14.2/vignettes/GOSemSim.Rmd	2020-04-27 20:23:20.000000000 +0000
+++ r-bioc-gosemsim-2.16.1/vignettes/GOSemSim.Rmd	2020-10-27 17:25:39.000000000 +0000
@@ -1,12 +1,10 @@
 ---
 title: "GO Semantic Similarity Analysis"
-author: "\\
-
-	Guangchuang Yu\\
-
-        School of Basic Medical Sciences, Southern Medical University"
+author: 
+- name: Guangchuang Yu
+  email: guangchuangyu@gmail.com
+  affiliation: Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University
 date: "`r Sys.Date()`"
-bibliography: GOSemSim.bib
 output:
   prettydoc::html_pretty:
     toc: true
@@ -15,7 +13,7 @@
   pdf_document:
     toc: true
 vignette: >
-  % \VignetteIndexEntry{An introduction to GOSemSim}
+  % \VignetteIndexEntry{GOSemSim}
   % \VignetteEngine{knitr::rmarkdown}
   % \usepackage[utf8]{inputenc}
   %\VignetteEncoding{UTF-8}
@@ -28,314 +26,28 @@
 ```
 
 ```{r echo=FALSE, results="hide", message=FALSE}
-library(org.Hs.eg.db)
-library(GO.db)
-library(GOSemSim)
-
 Biocpkg <- function (pkg) {
     sprintf("[%s](http://bioconductor.org/packages/%s)", pkg, pkg)
 }
 ```
 
-# Citation
-
-
-If you use `r Biocpkg('GOSemSim')` in published research, please cite:
-
-+ **Yu G**. [Gene Ontology Semantic Similarity Analysis Using GOSemSim](http://dx.doi.org/10.1007/978-1-0716-0301-7_11). In: Kidder B. (eds) Stem Cell Transcriptional Networks. **_Methods in Molecular Biology_**, 2020, 2117:207-215. Humana, New York, NY. 
-+ **Yu G**^#^, Li F^#^, Qin Y, Bo X^\*^, Wu Y and Wang S^\*^. [GOSemSim: an R package for measuring semantic similarity among GO terms and gene products](http://dx.doi.org/10.1093/bioinformatics/btq064). **_Bioinformatics_**. 2010, 26(7):976-978. 
-
-
-
-# Introduction
-
-Functional similarity of gene products can be estimated by controlled
-biological vocabularies, such as Gene Ontology (GO) and Disease Ontology (DO). GO comprises of
-three orthogonal ontologies, i.e. molecular function (MF), biological
-process (BP), and cellular component (CC).
-
-
-Four methods including Resnik[@philip_semantic_1999], Jiang[@jiang_semantic_1997],
-Lin[@lin_information-theoretic_1998] and Schlicker[@schlicker_new_2006] have been presented to determine the semantic similarity
-of two GO terms based on the annotation statistics of their common
-ancestor terms. Wang[@wang_new_2007]
-proposed a method to measure the similarity based on the graph structure of
-GO. Each of these methods has its own advantages and
-weaknesses.
-
-[GOSemSim](https://www.bioconductor.org/packages/GOSemSim) package[@yu2010] is developed to compute
-semantic similarity among GO terms, sets of GO terms, gene products,
-and gene clusters, providing five methods mentioned above. We have
-developed another package, [DOSE](https://www.bioconductor.org/packages/DOSE)[@yu_dose_2015], for measuring semantic
-similarity among Disease Ontology (DO) terms and gene products at disease perspective.
-
-
-# Semantic Similarity Measurement Based on GO
-
-## Information content-based methods
-
-Four methods proposed by Resnik[@philip_semantic_1999],
-Jiang[@jiang_semantic_1997], Lin[@lin_information-theoretic_1998]
-and Schlicker[@schlicker_new_2006] are information content (IC) based, which
-depend on the frequencies of two GO terms involved and that of their
-closest common ancestor term in a specific corpus of GO
-annotations. The information content of a GO term is computed by the
-negative log probability of the term occurring in GO corpus. A rarely used term contains a
-greater amount of information.
-
-The frequency of a term t is defined as:
-$p(t) = \frac{n_{t'}}{N} | t' \in \left\{t, \; children\: of\: t \right\}$
-
-where $n_{t'}$ is the number of term $t'$, and $N$ is the total number of terms in GO corpus.
-
-Thus the information content is defined as:
-$IC(t) = -\log(p(t))$
-
-As GO allow multiple parents for each concept, two terms can share
-parents by multiple paths. IC-based methods calculate similarity of two GO terms based on the information content of their closest common ancestor term, which was also called most informative common ancestor (MICA).
-
-### Resnik method
-
-The Resnik method is defined as:
-$sim_{Resnik}(t_1,t_2) = IC(MICA)$
-
-### Lin method
-
-The Lin method is defined as:
-$sim_{Lin}(t_1,t_2) = \frac{2IC(MICA)}{IC(t_1)+IC(t_2)}$
-
-### Rel method
-
-The Relevance method, which was proposed by Schlicker, combine Resnik's and Lin's method
-and is defined as:
-$sim_{Rel}(t_1,t_2) = \frac{2IC(MICA)(1-p(MICA))}{IC(t_1)+IC(t_2)}$
-
-### Jiang method
-
-The Jiang and Conrath's method is defined as:
-$sim_{Jiang}(t_1,t_2) = 1-\min(1, IC(t_1) + IC(t_2) - 2IC(MICA))$
-
-
-## Graph-based method
-Graph-based methods using the topology of GO graph structure to
-compute semantic similarity. Formally, a GO term A can be represented
-as $DAG_{A}=(A,T_{A},E_{A})$ where $T_{A}$ is the set of GO terms in
-$DAG_{A}$, including term A and all of its ancestor terms in the GO
-graph, and $E_{A}$ is the set of edges connecting the GO terms in
-$DAG_{A}$.
-
-### Wang method
-To encode the semantic of a GO term in a measurable format to enable a quantitative comparison, Wang[@wang_new_2007] firstly defined the semantic value of term A as the aggregate contribution of all terms in $DAG_{A}$ to the semantics of term A, terms closer to term A in $DAG_{A}$ contribute more to its semantics. Thus, defined the contribution of a GO term $t$ to the semantic of GO term $A$ as the S-value of GO term $t$ related to term $A$.
-For any of term $t$ in $DAG_{A}$, its S-value related to term $A$, $S_{A}(\textit{t})$ is defined as:
-
-
-$\left\{\begin{array}{l} S_{A}(A)=1 \\ S_{A}(\textit{t})=\max\{w_{e} \times S_{A}(\textit{t}') | \textit{t}' \in children \: of(\textit{t}) \} \; if \: \textit{t} \ne A \end{array} \right.$
-
-where $w_{e}$ is the semantic contribution factor for edge $e \in E_{A}$ linking term $t$ with its child term $t'$.
-Term $A$ contributes to its own is defined as 1. After obtaining the S-values for all terms in $DAG_{A}$,
-the semantic value of DO term A, $SV(A)$, is calculated as:
-
-$SV(A)=\displaystyle\sum_{t \in T_{A}} S_{A}(t)$
-
-Thus given two GO terms A and B, the semantic similarity between these two terms is defined as:
-
-$sim_{Wang}(A, B) = \frac{\displaystyle\sum_{t \in T_{A} \cap T_{B}}{S_{A}(t) + S_{B}(t)}}{SV(A) + SV(B)}$
-
-where $S_{A}(\textit{t})$ is the S-value of GO term $t$ related to term $A$
-and $S_{B}(\textit{t})$ is the S-value of GO term $t$ related to term $B$.
-
-This method proposed by Wang[@wang_new_2007] determines the semantic
-similarity of two GO terms based on both the locations of these terms
-in the GO graph and their relations with their ancestor terms.
-
-## Supported organisms
-
-For IC-based methods, information of GO term is species specific. We
-need to calculate `IC` for all GO terms of a species before we measure
-semantic
-similarity. [GOSemSim](https://www.bioconductor.org/packages/GOSemSim)
-support all organisms that have an `OrgDb` object available.
-
-Bioconductor have already provided `OrgDb` for about 20 species, see <http://bioconductor.org/packages/release/BiocViews.html#___OrgDb>.
-
-We can build query `OrgDb` online via [AnnotationHub](https://www.bioconductor.org/packages/AnnotationHub). For example:
-
-```{r eval=FALSE}
-library(AnnotationHub)
-hub <- AnnotationHub()
-q <- query(hub, "Cricetulus")
-id <- q$ah_id[length(q)]
-Cgriseus <- hub[[id]]
-```
-
-
-If organism is not supported by [AnnotationHub](https://www.bioconductor.org/packages/AnnotationHub), user can use [AnnotationForge](https://www.bioconductor.org/packages/AnnotationForge) to build `OrgDb`.
-
-Once we have `OrgDb`, we can build annotation data needed by [GOSemSim](https://www.bioconductor.org/packages/GOSemSim) via `godata` function.
-
-```{r}
-library(GOSemSim)
-hsGO <- godata('org.Hs.eg.db', ont="MF")
-```
-
-User can set `computeIC=FALSE` if they only want to use Wang's method.
-
-
-## goSim and mgoSim function
+# Vignette
 
-In [GOSemSim](https://www.bioconductor.org/packages/GOSemSim), we implemented all these IC-based and graph-based methods. __*goSim*__ function calculates semantic similarity between two GO terms, while __*mgoSim*__ function calculates semantic similarity between two sets of GO terms.
+Please go to <https://yulab-smu.top/biomedical-knowledge-mining-book/> for the full vignette.
 
 
-```{r}
-goSim("GO:0004022", "GO:0005515", semData=hsGO, measure="Jiang")
-goSim("GO:0004022", "GO:0005515", semData=hsGO, measure="Wang")
-go1 = c("GO:0004022","GO:0004024","GO:0004174")
-go2 = c("GO:0009055","GO:0005515")
-mgoSim(go1, go2, semData=hsGO, measure="Wang", combine=NULL)
-mgoSim(go1, go2, semData=hsGO, measure="Wang", combine="BMA")
-```
-
-# Gene Semantic Similarity Measurement
-
-On the basis of semantic similarity between GO terms, [GOSemSim](https://www.bioconductor.org/packages/GOSemSim) can
-also compute semantic similarity among sets of GO terms, gene products, and gene clusters.
-
-Suppose we have gene $g_1$ annotated by GO terms sets $GO_{1}=\{go_{11},go_{12} \cdots go_{1m}\}$
-and $g_2$ annotated by $GO_{2}=\{go_{21},go_{22} \cdots go_{2n}\}$, [GOSemSim](https://www.bioconductor.org/packages/GOSemSim) implemented four methods which called __*max*__, __*avg*__, __*rcmax*__, and __*BMA*__ to combine
-semantic similarity scores of multiple GO terms. The similarities
-among gene products and gene clusters which annotated by multiple GO
-terms were also calculated by the same combine methods mentioned
-above.
-
-
-## Combine methods
-
-### max
-
-The __*max*__ method calculates the maximum semantic similarity score over all pairs of GO terms between these two GO term sets.
-
-
-$sim_{max}(g_1, g_2) = \displaystyle\max_{1 \le i \le m, 1 \le j \le n} sim(go_{1i}, go_{2j})$
-
-### avg
-
-The __*avg*__ calculates the average semantic similarity score over all pairs of GO terms.
-
-
-$sim_{avg}(g_1, g_2) = \frac{\displaystyle\sum_{i=1}^m\sum_{j=1}^nsim(go_{1i}, go_{2j})}{m \times n}$
-
-### rcmax
-
-Similarities among two sets of GO terms form a matrix, the __*rcmax*__ method uses the maximum of _RowScore_ and _ColumnScore_, where _RowScore_ (or _ColumnScore_) is the average of maximum similarity on each row (or column).
-
-
-$sim_{rcmax}(g_1, g_2) = \max(\frac{\displaystyle\sum_{i=1}^m \max_{1 \le j \le n} sim(go_{1i}, go_{2j})}{m},\frac{\displaystyle\sum_{j=1}^n \max_{1 \le i \le m} sim(go_{1i},go_{2j})}{n})$
-
-### BMA
-
-The __*BMA*__ method, used the **B**est-**M**atch **A**verage strategy, calculates the average of all maximum similarities on each row and column, and is defined as:
-
-
-$sim_{BMA}(g_1, g_2) = \frac{\displaystyle\sum_{1=i}^m \max_{1 \le j \le n}sim(go_{1i}, go_{2j}) + \displaystyle\sum_{1=j}^n \max_{1 \le i \le m}sim(go_{1i}, go_{2j})} {m+n}$
-
-
-## geneSim and mgeneSim
-
-In [GOSemSim](https://www.bioconductor.org/packages/GOSemSim), we implemented __*geneSim*__ to calculate semantic similarity between two gene products, and __*mgeneSim*__ to calculate semantic similarity among multiple gene products.
-
-```{r}
-geneSim("241", "251", semData=hsGO, measure="Wang", combine="BMA")
-mgeneSim(genes=c("835", "5261","241", "994"),
-         semData=hsGO, measure="Wang",verbose=FALSE)
-mgeneSim(genes=c("835", "5261","241", "994"),
-       semData=hsGO, measure="Rel",verbose=FALSE)
-```
-
-By default, `godata` function use `ENTREZID` as keytype, and the input ID type is `ENTREZID`. User can use other ID types such as `ENSEMBL`, `UNIPROT`, `REFSEQ`, `ACCNUM`, `SYMBOL` _et al_.
-
-Here as an example, we use `SYMBOL` as `keytype` and calculate semantic similarities among several genes by using their gene symbol as input.
-
-```{r}
-hsGO2 <- godata('org.Hs.eg.db', keytype = "SYMBOL", ont="MF", computeIC=FALSE)
-genes <- c("CDC45", "MCM10", "CDC20", "NMU", "MMP1")
-mgeneSim(genes, semData=hsGO2, measure="Wang", combine="BMA", verbose=FALSE)
-```
-
-Users can also use [`clusterProfiler::bitr`](https://bioconductor.org/packages/release/bioc/vignettes/clusterProfiler/inst/doc/clusterProfiler.html#bitr-biological-id-translator) to translate biological IDs.
-
-
-## clusterSim and mclusterSim
-
-We also implemented __*clusterSim*__ for calculating semantic similarity between two gene clusters
-and __*mclusterSim*__ for calculating semantic similarities among multiple gene clusters.
-
-```{r}
-gs1 <- c("835", "5261","241", "994", "514", "533")
-gs2 <- c("578","582", "400", "409", "411")
-clusterSim(gs1, gs2, semData=hsGO, measure="Wang", combine="BMA")
-
-library(org.Hs.eg.db)
-x <- org.Hs.egGO
-hsEG <- mappedkeys(x)
-set.seed <- 123
-clusters <- list(a=sample(hsEG, 20), b=sample(hsEG, 20), c=sample(hsEG, 20))
-mclusterSim(clusters, semData=hsGO, measure="Wang", combine="BMA")
-```
-
-# Applications
-
-[GOSemSim](https://www.bioconductor.org/packages/GOSemSim) was cited by more than [200 papers](https://scholar.google.com.hk/scholar?oi=bibs&hl=en&cites=9484177541993722322,17633835198940746971,18126401808149291947) and had been applied to many research domains, including:
-
-+ [Disease or Drug analysis](https://guangchuangyu.github.io/software/GOSemSim/featuredArticles/#diease-or-drug-analysis)
-+ [Gene/Protein functional analysis](https://guangchuangyu.github.io/software/GOSemSim/featuredArticles/#geneprotein-functional-analysis)
-+ [Protein-Protein interaction](https://guangchuangyu.github.io/software/GOSemSim/featuredArticles/#protein-protein-interaction)
-+ [miRNA-mRNA interaction](https://guangchuangyu.github.io/software/GOSemSim/featuredArticles/#mirna-mrna-interaction)
-+ [sRNA regulation](https://guangchuangyu.github.io/software/GOSemSim/featuredArticles/#srna-regulation)
-+ [Evolution](https://guangchuangyu.github.io/software/GOSemSim/featuredArticles/#evolution)
-
-Find out more on <https://guangchuangyu.github.io/software/GOSemSim/featuredArticles/>.
-
-
-# GO enrichment analysis
-
-GO enrichment analysis can be supported by our package [clusterProfiler](https://www.bioconductor.org/packages/clusterProfiler)[@yu2012], which supports hypergeometric test and Gene Set Enrichment Analysis (GSEA). Enrichment results across different gene clusters can be compared using __*compareCluster*__ function.
-
-# Disease Ontology Semantic and Enrichment analysis
+# Citation
 
-Disease Ontology (DO) annotates human genes in the context of disease. DO is an important annotation in translating molecular findings from high-throughput data to clinical relevance.
-[DOSE](https://www.bioconductor.org/packages/DOSE)[@yu_dose_2015] supports semantic similarity computation among DO terms and genes.
-Enrichment analysis including hypergeometric model and GSEA are also implemented to support discovering disease associations of high-throughput biological data.
 
-# MeSH enrichment and semantic analyses
+If you use `r Biocpkg('GOSemSim')` in published research, please cite the most appropriate paper(s) from this list:
 
-MeSH (Medical Subject Headings) is the NLM controlled vocabulary used to manually index articles for MEDLINE/PubMed. [meshes](https://www.bioconductor.org/packages/meshes) supports enrichment (hypergeometric test and GSEA) and semantic similarity analyses for more than 70 species.
+1. **Yu G**. [Gene Ontology Semantic Similarity Analysis Using GOSemSim](http://dx.doi.org/10.1007/978-1-0716-0301-7_11). In: Kidder B. (eds) Stem Cell Transcriptional Networks. **_Methods in Molecular Biology_**, 2020, 2117:207-215. Humana, New York, NY. 
+2. **Yu G**^#^, Li F^#^, Qin Y, Bo X^\*^, Wu Y and Wang S^\*^. [GOSemSim: an R package for measuring semantic similarity among GO terms and gene products](http://dx.doi.org/10.1093/bioinformatics/btq064). **_Bioinformatics_**. 2010, 26(7):976-978. 
 
 
 
 # Need helps?
 
 
-If you have questions/issues, please visit
-[GOSemSim homepage](https://guangchuangyu.github.io/software/GOSemSim/) first.
-Your problems are mostly documented. If you think you found a bug, please follow
-[the guide](https://guangchuangyu.github.io/2016/07/how-to-bug-author/) and
-provide a reproducible example to be posted
-on
-[github issue tracker](https://github.com/GuangchuangYu/GOSemSim/issues).
-For questions, please post
-to [Bioconductor support site](https://support.bioconductor.org/) and tag your
-post with *GOSemSim*.
-
-
-For Chinese user, you can follow me on [WeChat (微信)](https://guangchuangyu.github.io/blog_images/biobabble.jpg).
-
-# Session Information
-
-Here is the output of `sessionInfo()` on the system on which this document was compiled:
-
-```{r echo=FALSE}
-sessionInfo()
-```
-
-# References
+For questions, please post to [Bioconductor support site](https://support.bioconductor.org/) and tag your post with *GOSemSim*.