Predicting possible directed-graph patterns of gene expressions in studies involving multiple treatments
Pages 579 - 581
Abstract
Analysis of gene expression data from studies to assess patterns of gene expression to multiple treatments is usually challenging due to the inadequacy of sample size. We introduce an approach of representing gene expression response to multiple treatments using directed graphs and establish a relationship between sample size and a graph property known as contractibility. We exploit this relationship to predict patterns of gene response using synthetic replicates generated from real samples to produce most probable patterns for each pattern observed based on experimental replicates.
Prediction based on gene expression simulation was validated with 4 different distribution models of gene expression, including Gaussian, Gaussian mixture, Weibull, and log normal. Across all distributions, we showed that predicting comparison outcomes was quite accurate, with accuracy generally above 0.85. Further, we showed how to apply this method to analyze gene responses to multiple treatments with few samples.
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- Predicting possible directed-graph patterns of gene expressions in studies involving multiple treatments
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Information & Contributors
Information
Published In
October 2012
725 pages
ISBN:9781450316705
DOI:10.1145/2382936
- General Chair:
- Sanjay Ranka,
- Program Chairs:
- Tamer Kahveci,
- Mona Singh
Copyright © 2012 Authors.
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Association for Computing Machinery
New York, NY, United States
Publication History
Published: 07 October 2012
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BCB' 12
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BCB' 12: ACM International Conference on Bioinformatics, Computational Biology and Biomedicine
October 7 - 10, 2012
Florida, Orlando
Acceptance Rates
BCB '12 Paper Acceptance Rate 33 of 159 submissions, 21%;
Overall Acceptance Rate 254 of 885 submissions, 29%
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