Group Title: CISE Technical Reports
Title: Leveraging Gene Networks to Estimate Perturbations on Gene Expression
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 Material Information
Title: Leveraging Gene Networks to Estimate Perturbations on Gene Expression
Series Title: CISE Technical Reports
Physical Description: Technical Reports
Creator: Nirmalya Bandyopadhyay
Manas Somaiya
Tamer Kahveci
Sanjay Ranka
Publisher: Computer and Information Science and Engineering, University of Florida
Publication Date: 2010
Abstract: External factors such as radiation, drugs or chemotherapy can alter the expressions of a set of genes. We call these genes the primarily affected genes. Primarily affected genes can in time change the expressions of other genes as they activate/suppress each other through interactions. Measuring the gene expressions before and after applying an external factor (i.e., perturbations) in microarray experiments can reveal how the expression of each gene changes. It however can not tell the cause of the change. In this paper, we consider the problem of identifying primarily affected genes given the expression measurements of a set of genes before and after the application of an external perturbation. We develop a new probabilistic method to quantify the cause of differential expression of each gene. Our method considers the possible gene interactions in regulatory and signaling networks, for a large number of perturbations. It uses a Bayesian model with the help of Markov Random Fields to capture the dependency between the genes. It also provides the underlying distribution of the impact with confidence interval. Our experiments on both real and synthetic datasets demonstrate that our method can find primarily affected genes with high accuracy. In our experiments, our method was 100% accurate when the difference between expected expressions of primarily and secondarily affected genes is at least half of the standard deviation of the gene expressions. Our experiments also suggest that our method is significantly more accurate then SSEM, a recent relevant method, and the Studet’s t-test.
Funding: Support for the development of the technical infrastructure and partner training provided by the United States Department of Education TICFIA program.
Acquisition: Collected for University of Florida's Institutional Repository by the UFIR Self-Submittal tool. Submitted by Dina Benson.
Publication Status: Published
 Record Information
Bibliographic ID: IR00000079
Volume ID: VID00001
Source Institution: University of Florida Institutional Repository
Rights Management: Permissions granted to the University of Florida Institutional Repository and University of Florida Digital Collections to allow use by the submitter. All rights reserved by the source institution.

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