A unified adaptive co-identification framework for high-D expression data

Shuzhong Zhang, Kun Wang, Cody Ashby, Bilian Chen, Xiuzhen Huang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Scopus citations

Abstract

High-throughput techniques are producing large-scale high-dimensional (e.g., 4D with genes vs timepoints vs conditions vs tissues) genome-wide gene expression data. This induces increasing demands for effective methods for partitioning the data into biologically relevant groups. Current clustering and co-clustering approaches have limitations, which may be very time consuming and work for only low-dimensional expression datasets. In this work, we introduce a new notion of "co-identification", which allows systematical identification of genes participating different functional groups under different conditions or different development stages. The key contribution of our work is to build a unified computational framework of co-identification that enables clustering to be high-dimensional and adaptive. Our framework is based upon a generic optimization model and a general optimization method termed Maximum Block Improvement. Testing results on yeast and Arabidopsis expression data are presented to demonstrate high efficiency of our approach and its effectiveness.

Original languageEnglish (US)
Title of host publicationPattern Recognition in Bioinformatics - 7th IAPR International Conference, PRIB 2012, Proceedings
Pages59-70
Number of pages12
DOIs
StatePublished - 2012
Event7th IAPR International Conference on Pattern Recognition in Bioinformatics, PRIB 2012 - Tokyo, Japan
Duration: Nov 8 2012Nov 10 2012

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume7632 LNBI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other7th IAPR International Conference on Pattern Recognition in Bioinformatics, PRIB 2012
CountryJapan
CityTokyo
Period11/8/1211/10/12

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