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- W2148985488 abstract "Nearly 2200 genomes that encode around 6 million proteins have now been sequenced. Around 40% of these proteins are of unknown function, even when function is loosely and minimally defined as 'belonging to a superfamily'. In addition to in silico methods, the swelling stream of high-throughput experimental data can give valuable clues for linking these unknowns with precise biological roles. The goal is to develop integrative data-mining platforms that allow the scientific community at large to access and utilize this rich source of experimental knowledge. To this end, we review recent advances in generating whole-genome experimental datasets, where this data can be accessed, and how it can be used to drive prediction of gene function." @default.
- W2148985488 created "2016-06-24" @default.
- W2148985488 creator A5013938980 @default.
- W2148985488 creator A5026118622 @default.
- W2148985488 date "2011-04-01" @default.
- W2148985488 modified "2023-10-17" @default.
- W2148985488 title "Mining high-throughput experimental data to link gene and function" @default.
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- W2148985488 doi "https://doi.org/10.1016/j.tibtech.2011.01.001" @default.
- W2148985488 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/3073767" @default.
- W2148985488 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/21310501" @default.
- W2148985488 hasPublicationYear "2011" @default.
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