期刊
AMERICAN JOURNAL OF PHYSIOLOGY-RENAL PHYSIOLOGY
卷 311, 期 4, 页码 F787-F792出版社
AMER PHYSIOLOGICAL SOC
DOI: 10.1152/ajprenal.00249.2016
关键词
data science; kidney physiology; systems biology; BIG data
资金
- Division of Intramural Research, National Heart, Lung, and Blood Institute [ZIA-HL001285, ZIA-HL006129]
Due to recent advances in high-throughput techniques, we and others have generated multiple proteomic and transcriptomic databases to describe and quantify gene expression, protein abundance, or cellular signaling on the scale of the whole genome/proteome in kidney cells. The existence of so much data from diverse sources raises the following question: How can researchers find information efficiently for a given gene product over all of these data sets without searching each data set individually? This is the type of problem that has motivated the Big-Data revolution in Data Science, which has driven progress in fields such as marketing. Here we present an online Big-Data tool called BIG (Biological Information Gatherer) that allows users to submit a single online query to obtain all relevant information from all indexed databases. BIG is accessible at http://big.nhlbi.nih.gov/.
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