September 28, 2018 Dataset Open Access

Stupp, Gregory S; Su, Andrew I

DCAT Export

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        <foaf:name>Stupp, Gregory S</foaf:name>
        <foaf:givenName>Gregory S</foaf:givenName>
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        <foaf:name>Su, Andrew I</foaf:name>
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    <dct:title>Drug Indications Extracted from FAERS</dct:title>
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    <dct:description>&lt;p&gt;This dataset contains drug indications extracted from the&amp;nbsp;FDA Adverse Event Reporting System (&lt;a href="https://www.fda.gov/drugs/guidancecomplianceregulatoryinformation/surveillance/adversedrugeffects/"&gt;FAERS&lt;/a&gt;).&lt;/p&gt; &lt;p&gt;Source code here:&amp;nbsp;&lt;a href="https://github.com/stuppie/faers"&gt;https://github.com/stuppie/faers&lt;/a&gt;&lt;/p&gt; &lt;p&gt;&lt;strong&gt;Method Outline&lt;/strong&gt;&lt;/p&gt; &lt;ul&gt; &lt;li&gt;Data files are extracted from zip files, parsed from csvs, and imported into a MySQL database (see&amp;nbsp;parser.py).&lt;/li&gt; &lt;li&gt;Duplicate records are then de-duplicated by taking only the most recent version for each case ID (see&amp;nbsp;dedupe.py).&lt;/li&gt; &lt;li&gt;Indications are normalized by matching to UMLS terms by string matching. Cross-references to Human Phenotype Ontology are pulled from UMLS and xrefs to Monarch Disease Ontology (MONDO) are pulled from MONDO using the UMLS xrefs. (See&amp;nbsp;normalize_indications.py)&lt;/li&gt; &lt;li&gt;Drugs names are normalized first by applying a few simple string cleaning operations (strip, fix slashes and periods). Then they are attempted to be matched to rxnorm by exact string matching. Those that don&amp;#39;t match are run against rxnorm&amp;#39;s &lt;a href="https://rxnav.nlm.nih.gov/RxNormAPIs.html#uLink=RxNorm_REST_getApproximateMatch"&gt;approximate matching service&lt;/a&gt;, and are accepted if the score is higher than&amp;nbsp;67/100. The matched rxnorm CUIs are then mapped to the their Ingredient level rxnorm ID. (See&amp;nbsp;normalize_drugs.py)&lt;/li&gt; &lt;li&gt;Indications are then retrieved for each drug ingredient and filtered to require a minimum of 20 individual occurances. (See&amp;nbsp;get_indications.py)&lt;/li&gt; &lt;/ul&gt;</dct:description>
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