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[ci skip] This build is based on 4d338ed. This commit was created by the following CI build and job: https://github.com/Benjamin-Lee/deep-rules/commit/4d338ed6591c956d3195054eb881ed8f11b69a21/checks https://github.com/Benjamin-Lee/deep-rules/runs/517168930
Benjamin-Lee · Jan 28, 2021 · f2df52a · f2df52a
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diff --git a/manuscript.html b/manuscript.html
@@ -24,7 +24,7 @@
   <meta name="author" content="Alexandra J. Lee" />
   <meta name="author" content="Beth Signal" />
   <meta name="author" content="Juan Jose Carmona" />
-  <meta name="dcterms.date" content="2021-01-23" />
+  <meta name="dcterms.date" content="2021-01-28" />
   <meta name="keywords" content="quick tips, machine learning, deep learning, artificial intelligence" />
   <title>Ten Quick Tips for Deep Learning in Biology</title>
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@@ -47,8 +47,8 @@
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   <meta property="twitter:title" content="Ten Quick Tips for Deep Learning in Biology" />
-  <meta name="dc.date" content="2021-01-23" />
-  <meta name="citation_publication_date" content="2021-01-23" />
+  <meta name="dc.date" content="2021-01-28" />
+  <meta name="citation_publication_date" content="2021-01-28" />
   <meta name="dc.language" content="en-US" />
   <meta name="citation_language" content="en-US" />
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@@ -135,13 +135,13 @@
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+  <link rel="alternate" type="text/html" href="https://Benjamin-Lee.github.io/deep-rules/v/4d338ed6591c956d3195054eb881ed8f11b69a21/" />
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@@ -153,10 +153,10 @@ <h1 class="title">Ten Quick Tips for Deep Learning in Biology</h1>
 </header>
 <p><small><em>
 This manuscript
-(<a href="https://Benjamin-Lee.github.io/deep-rules/v/cc09f2b48953fe4b7a52ad5ff955bfb0d25e62bd/">permalink</a>)
+(<a href="https://Benjamin-Lee.github.io/deep-rules/v/4d338ed6591c956d3195054eb881ed8f11b69a21/">permalink</a>)
 was automatically generated
-from <a href="https://github.com/Benjamin-Lee/deep-rules/tree/cc09f2b48953fe4b7a52ad5ff955bfb0d25e62bd">Benjamin-Lee/deep-rules@cc09f2b</a>
-on January 23, 2021.
+from <a href="https://github.com/Benjamin-Lee/deep-rules/tree/4d338ed6591c956d3195054eb881ed8f11b69a21">Benjamin-Lee/deep-rules@4d338ed</a>
+on January 28, 2021.
 </em></small></p>
 <h2 id="authors">Authors</h2>
 <p>Please note the current author order is chronological and does not reflect the final order.</p>
@@ -786,7 +786,7 @@ <h2 class="page_break_before" id="references">References</h2>
 <div id="ref-ndSzNxZQ">
 <p>29. <strong>apple/turicreate</strong> <br />
 Apple<br />
-(2021-01-23) <a href="https://github.com/apple/turicreate">https://github.com/apple/turicreate</a></p>
+(2021-01-28) <a href="https://github.com/apple/turicreate">https://github.com/apple/turicreate</a></p>
 </div>
 <div id="ref-ra8TSEHY">
 <p>30. <strong>Auto-Keras: An Efficient Neural Architecture Search System</strong> <br />

diff --git a/manuscript.md b/manuscript.md
@@ -22,7 +22,7 @@ author-meta:
 - Juan Jose Carmona
 bibliography:
 - content/manual-references.json
-date-meta: '2021-01-23'
+date-meta: '2021-01-28'
 header-includes: '<!--
 
   Manubot generated metadata rendered from header-includes-template.html.
@@ -41,9 +41,9 @@ header-includes: '<!--
 
   <meta property="twitter:title" content="Ten Quick Tips for Deep Learning in Biology" />
 
-  <meta name="dc.date" content="2021-01-23" />
+  <meta name="dc.date" content="2021-01-28" />
 
-  <meta name="citation_publication_date" content="2021-01-23" />
+  <meta name="citation_publication_date" content="2021-01-28" />
 
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@@ -217,19 +217,19 @@ header-includes: '<!--
 
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@@ -258,10 +258,10 @@ title: Ten Quick Tips for Deep Learning in Biology
 
 <small><em>
 This manuscript
-([permalink](https://Benjamin-Lee.github.io/deep-rules/v/cc09f2b48953fe4b7a52ad5ff955bfb0d25e62bd/))
+([permalink](https://Benjamin-Lee.github.io/deep-rules/v/4d338ed6591c956d3195054eb881ed8f11b69a21/))
 was automatically generated
-from [Benjamin-Lee/deep-rules@cc09f2b](https://github.com/Benjamin-Lee/deep-rules/tree/cc09f2b48953fe4b7a52ad5ff955bfb0d25e62bd)
-on January 23, 2021.
+from [Benjamin-Lee/deep-rules@4d338ed](https://github.com/Benjamin-Lee/deep-rules/tree/4d338ed6591c956d3195054eb881ed8f11b69a21)
+on January 28, 2021.
 </em></small>
 
 ## Authors

diff --git a/manuscript.pdf b/manuscript.pdf
diff --git a/references.json b/references.json
@@ -3005,20 +3005,8 @@
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     "type": "article-journal",
-    "title": "Correct machine learning on protein sequences: a peer-reviewing perspective",
-    "container-title": "Briefings in Bioinformatics",
-    "page": "831-840",
-    "volume": "17",
-    "issue": "5",
-    "source": "DOI.org (Crossref)",
-    "URL": "https://doi.org/f89ms7",
-    "DOI": "10.1093/bib/bbv082",
-    "ISSN": "1467-5463, 1477-4054",
-    "shortTitle": "Correct machine learning on protein sequences",
-    "journalAbbreviation": "Brief Bioinform",
-    "language": "en",
+    "id": "QobI7Hyv",
     "author": [
       {
         "family": "Walsh",
@@ -3036,37 +3024,25 @@
     "issued": {
       "date-parts": [
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-          "2016",
+          2016,
           9
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     },
-    "accessed": {
-      "date-parts": [
-        [
-          "2021",
-          1,
-          23
-        ]
-      ]
-    },
+    "container-title": "Briefings in Bioinformatics",
+    "DOI": "10.1093/bib/bbv082",
+    "volume": "17",
+    "issue": "5",
+    "page": "831-840",
+    "publisher": "Oxford University Press (OUP)",
+    "title": "Correct machine learning on protein sequences: a peer-reviewing perspective",
+    "URL": "https://doi.org/f89ms7",
     "PMID": "26411473",
     "note": "This CSL JSON Item was automatically generated by Manubot v0.4.1 using citation-by-identifier.\nstandard_id: doi:10.1093/bib/bbv082"
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-    "title": "Correcting for experiment-specific variability in expression compendia can remove underlying signals",
-    "container-title": "GigaScience",
-    "page": "giaa117",
-    "volume": "9",
-    "issue": "11",
-    "source": "DOI.org (Crossref)",
-    "abstract": "Abstract\r\n            \r\n              Motivation\r\n              In the past two decades, scientists in different laboratories have assayed gene expression from millions of samples. These experiments can be combined into compendia and analyzed collectively to extract novel biological patterns. Technical variability, or \"batch effects,\" may result from combining samples collected and processed at different times and in different settings. Such variability may distort our ability to extract true underlying biological patterns. As more integrative analysis methods arise and data collections get bigger, we must determine how technical variability affects our ability to detect desired patterns when many experiments are combined.\r\n            \r\n            \r\n              Objective\r\n              We sought to determine the extent to which an underlying signal was masked by technical variability by simulating compendia comprising data aggregated across multiple experiments.\r\n            \r\n            \r\n              Method\r\n              We developed a generative multi-layer neural network to simulate compendia of gene expression experiments from large-scale microbial and human datasets. We compared simulated compendia before and after introducing varying numbers of sources of undesired variability.\r\n            \r\n            \r\n              Results\r\n              The signal from a baseline compendium was obscured when the number of added sources of variability was small. Applying statistical correction methods rescued the underlying signal in these cases. However, as the number of sources of variability increased, it became easier to detect the original signal even without correction. In fact, statistical correction reduced our power to detect the underlying signal.\r\n            \r\n            \r\n              Conclusion\r\n              When combining a modest number of experiments, it is best to correct for experiment-specific noise. However, when many experiments are combined, statistical correction reduces our ability to extract underlying patterns.",
-    "URL": "https://doi.org/ghhtpf",
-    "DOI": "10.1093/gigascience/giaa117",
-    "ISSN": "2047-217X",
-    "language": "en",
+    "id": "1GGrbeMvT",
     "author": [
       {
         "family": "Lee",
@@ -3092,21 +3068,20 @@
     "issued": {
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+          2020,
           11,
           3
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-          "2021",
-          1,
-          23
-        ]
-      ]
-    },
+    "container-title": "GigaScience",
+    "DOI": "10.1093/gigascience/giaa117",
+    "volume": "9",
+    "issue": "11",
+    "page": "giaa117",
+    "publisher": "Oxford University Press (OUP)",
+    "title": "Correcting for experiment-specific variability in expression compendia can remove underlying signals",
+    "URL": "https://doi.org/ghhtpf",
     "PMCID": "PMC7607552",
     "PMID": "33140829",
     "note": "This CSL JSON Item was automatically generated by Manubot v0.4.1 using citation-by-identifier.\nstandard_id: doi:10.1093/gigascience/giaa117"
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diff --git a/spelling-errors.txt b/spelling-errors.txt
@@ -97,13 +97,13 @@ multilayer
 Nuffield
 operationalize
 overfit
-overfitting
+Overfitting
 overinterpretation
 parallelized
 perceptrons
 pharmacovigilance
 phenotypes
-Pre
+pre
 pretraining
 pseudorandom
 pstew
@@ -126,7 +126,7 @@ TODO
 toolset
 TPOT
 transformative
-translational
+Translational
 triaged
 Triche
 Tschira