期刊
FEBS LETTERS
卷 590, 期 15, 页码 2327-2341出版社
WILEY
DOI: 10.1002/1873-3468.12307
关键词
computational prediction; molecular mechanism of disease; protein function; variant effect
资金
- Alexander von Humboldt foundation through the German Ministry for Research and Education (BMBF: Bundesministerium fuer Bildung und Forschung)
- NIH [R01 MH105524, U01 GM115486, U24 MH068457]
- NSF [DBI-1458477]
- USDA-NIFA [1015:0228906]
- Div Of Biological Infrastructure
- Direct For Biological Sciences [1458477] Funding Source: National Science Foundation
Precision medicine and personalized health efforts propose leveraging complex molecular, medical and family history, along with other types of personal data toward better life. We argue that this ambitious objective will require advanced and specialized machine learning solutions. Simply skimming some low-hanging results off the data wealth might have limited potential. Instead, we need to better understand all parts of the system to define medically relevant causes and effects: how do particular sequence variants affect particular proteins and pathways? How do these effects, in turn, cause the health or disease-related phenotype? Toward this end, deeper understanding will not simply diffuse from deeper machine learning, but from more explicit focus on understanding protein function, context-specific protein interaction networks, and impact of variation on both.
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