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Reprint of “Abstraction for data integration: Fusing mammalian molecular, cellular and phenotype big datasets for better knowledge extraction” ☆

Paper ID Volume ID Publish Year Pages File Format Full-Text
15037 1368 2015 16 PDF Available
Title
Reprint of “Abstraction for data integration: Fusing mammalian molecular, cellular and phenotype big datasets for better knowledge extraction” ☆
Abstract

•A small fraction of biomedical Big Data is converted to useful knowledge or reused.•Overview of a collection of structured mostly molecular mammalian biomedical Big Data resources.•Biases within data from these resources are suspected.•Data abstraction to attribute tables, networks and gene-sets enables reuse of biomedical datasets for integrative analyses.•Once data is abstracted it can be integrated and analyzed using supervised, unsupervised and integrative methods.

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.

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Keywords
Data integration; Bioinformatics; Systems biology; Systems pharmacology; Network biology
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Reprint of “Abstraction for data integration: Fusing mammalian molecular, cellular and phenotype big datasets for better knowledge extraction” ☆
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Publisher
Database: Elsevier - ScienceDirect
Journal: Computational Biology and Chemistry - Volume 59, Part B, December 2015, Pages 123–138
Authors
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Subjects
Physical Sciences and Engineering Chemical Engineering Bioengineering
Get Full-Text Now
Don't Miss Today's Special Offer
Price was $35.95
You save - $31
Price after discount Only $4.95
100% Money Back Guarantee
Full-text PDF Download
Online Support
Any Questions? feel free to contact us