DOI: 10.5061/dryad.gn219
title: Data from: The implicitome: a resource for rationalizing gene-disease associations

description: High-throughput experimental methods such as medical sequencing and
 genome-wide association studies (GWAS) identify increasingly large numbers
 of potential relations between genetic variants and diseases. Both
 biological complexity (millions of potential gene-disease associations)
 and the accelerating rate of data production necessitate computational
 approaches to prioritize and rationalize potential gene-disease relations.
 Here, we use concept profile technology to expose from the biomedical
 literature both explicitly stated gene-disease relations (the explicitome)
 and a much larger set of implied gene-disease associations (the
 implicitome). Implicit relations are largely unknown to, or are even
 unintended by the original authors, but they vastly extend the reach of
 existing biomedical knowledge for identification and interpretation of
 gene-disease associations. The implicitome can be used in conjunction with
 experimental data resources to rationalize both known and novel
 associations. We demonstrate the usefulness of the implicitome by
 rationalizing known and novel gene-disease associations, including those
 from GWAS. To facilitate the re-use of implicit gene-disease associations,
 we publish our data in compliance with FAIR Data Publishing
 recommendations [https://www.force11.org/group/fairgroup] using
 nanopublications. An online tool (http://knowledge.bio) is available to
 explore established and potential gene-disease associations in the context
 of other biomedical relations.
description: PMID
 listlist of PMIDs used to
 create concept profiles; gzipped file, one PMID per
 lineRestricted1980tillnow.PMIDs.gzMedline concept indexConcept index of Medline articles as specified in http://dx.doi.org/10.5061/dryad.gn219/1Medline1980till17Jul2014_UMLS2010ABHomologeneJochemToxV1_6.zipOverview of software packagesSoftware packages used for the various analyses and figures. Links are clickable and point to the repository for that code or data.Copy of Gene-disease paper - code overview.pdfExplicit associations (co-occurrence) nanopublicationsSet of 7,858,449 nanopublications, each of which asserts a single gene-disease co-occurrence and the PubMed ID of the abstract in which they co-occurgd-coocc.nq.gzAll associations as nanopublicationsThe complete set of all ~204 million associations (explicit and implicit) as nanopublications. Each nanopublication asserts an association between a gene and a disease concept and the percentile rank of the match score.gda-np.nq.gzAll associations as CSVAll ~204 million associations (explicit and implicit) listed as a CSV text file. Note that concept pairs are specified by concept ID and its first label according to our thesaurus.matchscores.release.csv.gzPre-compiled software dependenciesCollection of JAR files containing the dependencies for generating concept profiles and match scores. Source code is also available in our github repository.libraries.ziphttps://github.com/BiosemanticsDotOrg/GeneDiseasePaperSource code for generating and analyzing concept profiles is available from Github under a AGPLV3.0 license: https://github.com/BiosemanticsDotOrg/GeneDiseasePaper