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Activity Number:
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464
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Type:
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Topic Contributed
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Date/Time:
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Wednesday, August 1, 2007 : 2:00 PM to 3:50 PM
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Sponsor:
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Section on Physical and Engineering Sciences
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| Abstract - #309714 |
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Title:
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Dimension Reduction in Systems Biology
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Author(s):
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Lei Zhu*+ and Kwan Lee and Amit Bhattacharyya and Edit Kurali and Amber Anderson
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Companies:
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GlaxoSmithKline and GlaxoSmithKline and GlaxoSmithKline and GlaxoSmithKline and GlaxoSmithKline
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Address:
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5 Moore Drive, Research Triangle Park, NC, 27709,
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Keywords:
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Multivariate Analysis ; Omics Platform ; Selection Bias ; Supervised Analysis ; Unsupervised Analysis ; Univariate Analysis
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Abstract:
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Pharmaceutical industry is facing critical challenges in translational efficacy failure. Systems biology emerged as a paradigm shift from target-focused drug discovery to a systems approach. The automated acquisition of large amounts of omics data creates exploratory and interpretative analysis challenges in our attempt to unravel associations among blood chemistry, transcripts, proteins, metabolites, and lipids under the drug or disease perturbations. This paper discusses the various analysis approaches for dimension reduction on systems biology data to identify drug and/or disease biomarkers for better understanding of drug efficacy. The analysis approaches fall into four categories: univariate unsupervised or supervised, multivariate unsupervised or supervised. Application examples of these approaches at different stages of data analysis will be demonstrated.
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