JOURNAL ARTICLE

Phenome-driven disease genetics prediction toward drug discovery

Yang ChenLi LiGuo‐Qiang ZhangRong Xu

Year: 2015 Journal:   Bioinformatics Vol: 31 (12)Pages: i276-i283   Publisher: Oxford University Press

Abstract

Abstract Motivation: 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. Results: To demonstrate our approach, we explored a new phenotype database from biomedical ontologies and constructed Disease Manifestation Network (DMN). We combined DMN with mimMiner, which was a widely used phenotype database in disease gene prediction studies. Our approach achieved significantly improved performance over a baseline method, which used only one phenotype data source. In the leave-one-out cross-validation and de novo gene prediction analysis, our approach achieved the area under the curves of 90.7% and 90.3%, which are significantly higher than 84.2% (P < e−4) and 81.3% (P < e−12) for the baseline approach. We further demonstrated that our predicted genes have the translational potential in drug discovery. We used Crohn’s disease as an example and ranked the candidate drugs based on the rank of drug targets. Our gene prediction approach prioritized druggable genes that are likely to be associated with Crohn’s disease pathogenesis, and our rank of candidate drugs successfully prioritized the Food and Drug Administration-approved drugs for Crohn’s disease. We also found literature evidence to support a number of drugs among the top 200 candidates. In summary, we demonstrated that a novel strategy combining unique disease phenotype data with system approaches can lead to rapid drug discovery. Availability and implementation: nlp.case.edu/public/data/DMN Contact: [email protected]

Keywords:
Disease Druggability Phenome Drug discovery Computational biology Phenotype Drug development Candidate gene Gene Biology Drug Bioinformatics Genetics Medicine Pharmacology

Metrics

40
Cited By
3.61
FWCI (Field Weighted Citation Impact)
66
Refs
0.94
Citation Normalized Percentile
Is in top 1%
Is in top 10%

Citation History

Topics

Bioinformatics and Genomic Networks
Life Sciences →  Biochemistry, Genetics and Molecular Biology →  Molecular Biology
Gene expression and cancer classification
Life Sciences →  Biochemistry, Genetics and Molecular Biology →  Molecular Biology
Ferroptosis and cancer prognosis
Health Sciences →  Medicine →  Pulmonary and Respiratory Medicine
© 2026 ScienceGate Book Chapters — All rights reserved.