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Reprint of "Abstraction for data integration: Fusing mammalian molecular, cellular and phenotype big datasets for better knowledge extraction".

Abstract：

:With advances in genomics, transcriptomics, metabolomics and proteomics, and more expansive electronic clinical record monitoring, as well as advances in computation, we have entered the Big Data era in biomedical research. Data gathering is growing rapidly while only a small fraction of this data is converted to useful knowledge or reused in future studies. To improve this, an important concept that is often overlooked is data abstraction. To fuse and reuse biomedical datasets from diverse resources, data abstraction is frequently required. Here we summarize some of the major Big Data biomedical research resources for genomics, proteomics and phenotype data, collected from mammalian cells, tissues and organisms. We then suggest simple data abstraction methods for fusing this diverse but related data. Finally, we demonstrate examples of the potential utility of such data integration efforts, while warning about the inherit biases that exist within such data.

journal_name

Comput Biol Chem

journal_title

Computational biology and chemistry

authors

Rouillard AD,Wang Z,Ma'ayan A

doi

10.1016/j.compbiolchem.2015.08.005

subject

Has Abstract

pub_date

2015-12-01 00:00:00

pages

123-38

eissn

1476-9271

issn

1476-928X

pii

S1476-9271(15)00083-3

journal_volume

59 Pt B

pub_type

杂志文章,评审

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