期刊
GENETIC EPIDEMIOLOGY
卷 38, 期 4, 页码 275-280出版社
WILEY
DOI: 10.1002/gepi.21802
关键词
evolutionary computing; ENCODE; machine learning; pathway analysis
资金
- NIH grants [LM010098, LM009012, LM011360, EY022300, GM103506, GM103534]
- European Union FP7 Grant [601456-2]
The recently completed ENCODE project is a new source of information on metabolic activity, unveiling knowledge about evolution and similarities among species, refuting the myth that most DNA is junk and has no actual function. With this expansive resource comes a challenge: integrating these new layers of information into our current knowledge of single-nucleotide polymorphisms and previously described metabolic pathways with the aim of discovering new genes and pathways related to human diseases and traits. Further, we must determine which computational methods will be most useful in this pursuit. In this paper, we speculate over the possible methods that will emerge in this new, challenging field.
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