research-article

Differential biclustering for gene expression analysis

Authors:

Chandan K. Reddy,

Craig N. GirouxAuthors Info & Claims

BCB '10: Proceedings of the First ACM International Conference on Bioinformatics and Computational Biology

Pages 275 - 284

https://doi.org/10.1145/1854776.1854815

Published: 02 August 2010 Publication History

Abstract

Biclustering algorithms have been successfully used to find subsets of co-expressed genes under subsets of conditions. In some cases, microarray experiments are performed to compare the biological activities of the genes between two classes of cells, such as normal and cancer cells. In this paper, we propose DiBiCLUS, a novel Differential Biclustering algorithm, to identify differential biclusters from the gene expression data where the samples belong to one of the two classes. The genes in these differential biclusters can be positively or negatively co-expressed. We introduce two criteria for any pair of genes to be considered as a differential pair across the two classes. To illustrate the performance of the proposed algorithm, we present the experimental results of applying DiBiCLUS algorithm on synthetic and reallife datasets. These experiments show that the identified differential biclusters are both statistically and biologically significant.

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Cited By

Mandal KSarmah RBhattacharyya D(2021)POPBic: Pathway-Based Order Preserving Biclustering Algorithm Towards the Analysis of Gene Expression DataIEEE/ACM Transactions on Computational Biology and Bioinformatics10.1109/TCBB.2020.298081618:6(2659-2670)Online publication date: 1-Nov-2021
https://doi.org/10.1109/TCBB.2020.2980816
Wang MLiu YXiao GWang G(2019)Potential Hazardous Elements Mining for Task Synthesis Safety Analysis in IMA System2019 IEEE/AIAA 38th Digital Avionics Systems Conference (DASC)10.1109/DASC43569.2019.9081736(1-5)Online publication date: Sep-2019
https://doi.org/10.1109/DASC43569.2019.9081736
Zhang LWang MZhai ZWang G(2014)Efficient Mining Maximal Variant Usage and Low Usage Biclusters in Discrete Function-Resource MatrixJournal of Computers10.4304/jcp.9.5.1159-11679:5Online publication date: 1-May-2014
https://doi.org/10.4304/jcp.9.5.1159-1167
Show More Cited By

Differential biclustering for gene expression analysis
1. Applied computing
  1. Life and medical sciences
2. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Unsupervised learning

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cover image ACM Conferences

BCB '10: Proceedings of the First ACM International Conference on Bioinformatics and Computational Biology

August 2010

705 pages

ISBN:9781450304382

DOI:10.1145/1854776

General Chairs:
Aidong Zhang
SUNY at Buffalo
,
Mark Borodovsky
Georgia Tech
,
Program Chairs:
Gultekin Ozsoyoglu
Case Western Reserve University
,
Armin Mikler
University of North Texas

Copyright © 2010 ACM.

Permission to make digital or hard copies of all or part 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 components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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SIGBio: ACM Special Interest Group on Bioinformatics

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Association for Computing Machinery

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Published: 02 August 2010

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BCB'10: ACM International Conference on Bioinformatics and Computational Biology

August 2 - 4, 2010

New York, Niagara Falls

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Overall Acceptance Rate 254 of 885 submissions, 29%

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Cited By

Mandal KSarmah RBhattacharyya D(2021)POPBic: Pathway-Based Order Preserving Biclustering Algorithm Towards the Analysis of Gene Expression DataIEEE/ACM Transactions on Computational Biology and Bioinformatics10.1109/TCBB.2020.298081618:6(2659-2670)Online publication date: 1-Nov-2021
https://doi.org/10.1109/TCBB.2020.2980816
Wang MLiu YXiao GWang G(2019)Potential Hazardous Elements Mining for Task Synthesis Safety Analysis in IMA System2019 IEEE/AIAA 38th Digital Avionics Systems Conference (DASC)10.1109/DASC43569.2019.9081736(1-5)Online publication date: Sep-2019
https://doi.org/10.1109/DASC43569.2019.9081736
Zhang LWang MZhai ZWang G(2014)Efficient Mining Maximal Variant Usage and Low Usage Biclusters in Discrete Function-Resource MatrixJournal of Computers10.4304/jcp.9.5.1159-11679:5Online publication date: 1-May-2014
https://doi.org/10.4304/jcp.9.5.1159-1167
Odibat OReddy C(2014)Efficient mining of discriminative co-clusters from gene expression dataKnowledge and Information Systems10.1007/s10115-013-0684-041:3(667-696)Online publication date: 1-Dec-2014
https://dl.acm.org/doi/10.1007/s10115-013-0684-0
Anand RReddy C(2011)Constrained logistic regression for discriminative pattern miningProceedings of the 2011th European Conference on Machine Learning and Knowledge Discovery in Databases - Volume Part I10.5555/3121838.3121857(92-107)Online publication date: 5-Sep-2011
https://dl.acm.org/doi/10.5555/3121838.3121857
Anand RReddy C(2011)Constrained logistic regression for discriminative pattern miningProceedings of the 2011 European conference on Machine learning and knowledge discovery in databases - Volume Part I10.5555/2034063.2034082(92-107)Online publication date: 5-Sep-2011
https://dl.acm.org/doi/10.5555/2034063.2034082
Odibat OReddy CGrossman RRzhetsky AKim SWang W(2011)Ranking differential genes in co-expression networksProceedings of the 2nd ACM Conference on Bioinformatics, Computational Biology and Biomedicine10.1145/2147805.2147849(350-354)Online publication date: 1-Aug-2011
https://dl.acm.org/doi/10.1145/2147805.2147849
Anand RReddy C(2011)Constrained Logistic Regression for Discriminative Pattern MiningMachine Learning and Knowledge Discovery in Databases10.1007/978-3-642-23780-5_16(92-107)Online publication date: 2011
https://doi.org/10.1007/978-3-642-23780-5_16

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