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Rouillard A.D.,Mount Sinai School of Medicine | Rouillard A.D.,2ta Coordination and Integration Center | Rouillard A.D.,Illuminating the Druggable Genome Knowledge Management Center | Wang Z.,Mount Sinai School of Medicine | And 5 more authors.
Computational Biology and Chemistry | Year: 2015

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. © 2015 Elsevier Ltd. All rights reserved. Source


Rouillard A.D.,Mount Sinai School of Medicine | Rouillard A.D.,2ta Coordination and Integration Center | Rouillard A.D.,Illuminating the Druggable Genome Knowledge Management Center | Wang Z.,Mount Sinai School of Medicine | And 5 more authors.
Computational Biology and Chemistry | Year: 2015

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. © 2015 Elsevier Ltd. All rights reserved. Source

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