TY - GEN
T1 - A unified adaptive co-identification framework for high-D expression data
AU - Zhang, Shuzhong
AU - Wang, Kun
AU - Ashby, Cody
AU - Chen, Bilian
AU - Huang, Xiuzhen
N1 - Copyright:
Copyright 2012 Elsevier B.V., All rights reserved.
PY - 2012
Y1 - 2012
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84868708561&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84868708561&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-34123-6_6
DO - 10.1007/978-3-642-34123-6_6
M3 - Conference contribution
AN - SCOPUS:84868708561
SN - 9783642341229
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 59
EP - 70
BT - Pattern Recognition in Bioinformatics - 7th IAPR International Conference, PRIB 2012, Proceedings
T2 - 7th IAPR International Conference on Pattern Recognition in Bioinformatics, PRIB 2012
Y2 - 8 November 2012 through 10 November 2012
ER -