title: Gene-disease association with literature based enrichment.
abstract: BACKGROUND
  Gene set enrichment analysis (GSEA) annotates gene microarray data with functional information from the biomedical literature to improve gene-disease association prediction. We hypothesize that supplementing GSEA with comprehensive gene function catalogs built automatically using information extracted from the scientific literature will significantly enhance GSEA prediction quality.
  
  
  METHODS
  Gold standard gene sets for breast cancer (BrCa) and colorectal cancer (CRC) were derived from the literature. Two gene function catalogs (CMeSH and CUMLS) were automatically generated. 1. By using Entrez Gene to associate all recorded human genes with PubMed article IDs. 2. Using the genes mentioned in each PubMed article and associating each with the article's MeSH terms (in CMeSH) and extracted UMLS concepts (in CUMLS). Microarray data from the Gene Expression Omnibus for BrCa and CRC was then annotated using CMeSH and CUMLS and for comparison, also with several pre-existing catalogs (C2, C4 and C5 from the Molecular Signatures Database). Ranking was done using, a standard GSEA implementation (GSEA-p). Gene function predictions for enriched array data were evaluated against the gold standard by measuring area under the receiver operating characteristic curve (AUC).
  
  
  RESULTS
  Comparison of ranking using the literature enrichment catalogs, the pre-existing catalogs as well as five randomly generated catalogs show the literature derived enrichment catalogs are more effective. The AUC for BrCa using the unenriched gene expression dataset was 0.43, increasing to 0.89 after gene set enrichment with CUMLS. The AUC for CRC using the unenriched gene expression dataset was 0.54, increasing to 0.9 after enrichment with CMeSH. C2 increased AUC (BrCa 0.76, CRC 0.71) but C4 and C5 performed poorly (between 0.35 and 0.5). The randomly generated catalogs also performed poorly, equivalent to random guessing.
  
  
  CONCLUSIONS
  Gene set enrichment significantly improved prediction of gene-disease association. Selection of enrichment catalog had a substantial effect on prediction accuracy. The literature based catalogs performed better than the MSigDB catalogs, possibly because they are more recent. Catalogs generated automatically from the literature can be kept up to date.
  
  
  CONCLUSIONS
  Prediction of gene-disease association is a fundamental task in biomedical research. GSEA provides a promising method when using literature-based enrichment catalogs.
  
  
  BACKGROUND
  The literature based catalogs generated and used in this study are available from http://www2.chi.unsw.edu.au/literature-enrichment.
authors.affiliation: Centre for Health Informatics, University of New South Wales, Sydney, Australia. Electronic address: guyt@unsw.edu.au.
authors.forename: Guy
authors.initials: G
authors.lastname: Tsafnat
authors.affiliation: Centre for Health Informatics, University of New South Wales, Sydney, Australia.
authors.forename: Dennis
authors.initials: D
authors.lastname: Jasch
authors.affiliation: Centre for Health Informatics, University of New South Wales, Sydney, Australia.
authors.forename: Agam
authors.initials: A
authors.lastname: Misra
authors.affiliation: Centre for Health Informatics, University of New South Wales, Sydney, Australia.
authors.forename: Miew Keen
authors.initials: MK
authors.lastname: Choong
authors.affiliation: Centre for Health Informatics, University of New South Wales, Sydney, Australia.
authors.forename: Frank P-Y
authors.initials: FP
authors.lastname: Lin
authors.affiliation: Centre for Health Informatics, University of New South Wales, Sydney, Australia.
authors.forename: Enrico
authors.initials: E
authors.lastname: Coiera
mesh_terms: D001943:Breast Neoplasms / Q000235:genetics; D015179:Colorectal Neoplasms / Q000235:genetics; D005260:Female; D020022:Genetic Predisposition to Disease; D055106:Genome-Wide Association Study; D006801:Humans
keywords: Gene set enrichment analysis; Gene–disease association; High-throughput data analysis; Literature based discovery