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Phenome-driven disease genetics prediction toward drug discovery.

Yang Chen | Li Li | Guo-Qiang Zhang | Rong Xu
Bioinformatics (Oxford, England) | 2015

Discerning genetic contributions to diseases not only enhances our understanding of disease mechanisms, but also leads to translational opportunities for drug discovery. Recent computational approaches incorporate disease phenotypic similarities to improve the prediction power of disease gene discovery. However, most current studies used only one data source of human disease phenotype. We present an innovative and generic strategy for combining multiple different data sources of human disease phenotype and predicting disease-associated genes from integrated phenotypic and genomic data.

Pubmed ID: 26072493

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Associated grants

  • Agency: NICHD NIH HHS, United States
    Id: DP2 HD084068
  • Agency: NCI NIH HHS, United States
    Id: R25 CA094186
  • Agency: NCCDPHP CDC HHS, United States
    Id: DP2HD084068
  • Agency: NCI NIH HHS, United States
    Id: R25 CA094186-06

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DrugBank (tool)

RRID:SCR_002700

Bioinformatics and cheminformatics database that combines detailed drug (i.e. chemical, pharmacological and pharmaceutical) data with comprehensive drug target (i.e. sequence, structure, and pathway) information.

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