Gene-set profiles: visualizing dissimilarity within gene co-expression networks for biomarker identification
Authors: 
Saila Shama, Philip Lu, Narumol Doungpan, Asawin Meechai, Jonathan H. ChanAuthors Info & Claims
Saila Shama, Philip Lu, Narumol Doungpan, Asawin Meechai, Jonathan H. ChanAuthors Info & ClaimsPages 79 - 80
Abstract
We present a method to visualize gene co-expression from microarray data by plotting profiles of dissimilarity within gene-sets of biological pathways. A gene co-expression network is created by computing the correlation between each gene pair in a gene-set. We transform the networks into scale-free networks in order to calculate the dissimilarity weights that are used to create our profiles. Our approach further distinguishes between gene pairs consisting of both, one, or no statistically significant genes. We find that the shapes and density of the profiles provide useful information for identification of disease gene biomarkers. Our results provide a means of visualizing the overall distribution of gene dissimilarity for each gene-set, as well as how gene dissimilarity is linked to the mutual significance of gene pairs within a gene-set.
References
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Index Terms
- Gene-set profiles: visualizing dissimilarity within gene co-expression networks for biomarker identification
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Information & Contributors
Information
Published In
August 2017
158 pages
ISBN:9781450352925
DOI:10.1145/3105971
- General Chair:
- Robert P. Biuk-Aghai,
- Program Chairs:
- Jie Li,
- Shigeo Takahashi
Copyright © 2017 Owner/Author.
Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.
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- KMUTT: King Mongkut's University of Technology Thonburi
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Association for Computing Machinery
New York, NY, United States
Publication History
Published: 14 August 2017
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VINCI '17
Sponsor:
- KMUTT
VINCI '17: 10th International Symposium on Visual Information Communication and Interaction
August 14 - 16, 2017
Bangkok, Thailand
Acceptance Rates
VINCI '17 Paper Acceptance Rate 12 of 27 submissions, 44%;
Overall Acceptance Rate 71 of 193 submissions, 37%
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