| Literature DB >> 27279488 |
Yue Zhao1, Chin-Rang Yang1, Viswanathan Raghuram1, Jaya Parulekar1, Mark A Knepper2.
Abstract
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/.Keywords: BIG data; data science; kidney physiology; systems biology
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Year: 2016 PMID: 27279488 PMCID: PMC5142235 DOI: 10.1152/ajprenal.00249.2016
Source DB: PubMed Journal: Am J Physiol Renal Physiol ISSN: 1522-1466