Journal
BRIEFINGS IN BIOINFORMATICS
Volume 20, Issue 5, Pages 1769-1780Publisher
OXFORD UNIV PRESS
DOI: 10.1093/bib/bby049
Keywords
disease similarity; nosology; omics; networks; text mining; integrative
Funding
- Burroughs Wellcome Fund
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A fundamental challenge of modern biomedical research is understanding how diseases that are similar on the phenotypic level are similar on the molecular level. Integration of various genomic data sets with the traditionally used phenotypic disease similarity revealed novel genetic and molecular mechanisms and blurred the distinction between monogenic (Mendelian) and complex diseases. Network-based medicine has emerged as a complementary approach for identifying disease-causing genes, genetic mediators, disruptions in the underlying cellular functions and for drug repositioning. The recent development of machine and deep learning methods allow for leveraging real-life information about diseases to refine genetic and phenotypic disease relationships. This review describes the historical development and recent methodological advancements for studying disease classification (nosology).
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