期刊
SCIENTIFIC REPORTS
卷 3, 期 -, 页码 -出版社
NATURE PUBLISHING GROUP
DOI: 10.1038/srep03202
关键词
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资金
- European Research Council (ERC) [278212]
- National Science Foundation (NSF) Cyber-Enabled Discovery and Innovation (CDI) [OIA-1028394]
- NIH [P01 HD39691]
- GlaxoSmithKline (GSK) Research and Development Ltd
- Slovenian Research Agency [P2-0209]
- EU [Health-F5-2010-242038]
- ARRS [J1-5454]
- Serbian Ministry of Education and Science [III44006]
- Office Of The Director
- Office of Integrative Activities [1028394] Funding Source: National Science Foundation
The advent of genome-scale genetic and genomic studies allows new insight into disease classification. Recently, a shift was made from linking diseases simply based on their shared genes towards systems-level integration of molecular data. Here, we aim to find relationships between diseases based on evidence from fusing all available molecular interaction and ontology data. We propose a multi-level hierarchy of disease classes that significantly overlaps with existing disease classification. In it, we find 14 disease-disease associations currently not present in Disease Ontology and provide evidence for their relationships through comorbidity data and literature curation. Interestingly, even though the number of known human genetic interactions is currently very small, we find they are the most important predictor of a link between diseases. Finally, we show that omission of any one of the included data sources reduces prediction quality, further highlighting the importance in the paradigm shift towards systems-level data fusion.
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