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Biclustering of microarray data with MOSPO based on crowding distance.

Abstract High-throughput microarray technologies have generated and accumulated massive amounts of gene expression datasets that contain expression levels of thousands of genes under hundreds of different experimental conditions. The microarray datasets are usually presented in 2D matrices, where rows represent genes and columns represent experimental conditions. The analysis of such datasets can discover local structures composed by sets of genes that show coherent expression patterns under subsets of experimental conditions. It leads to the development of sophisticated algorithms capable of extracting novel and useful knowledge from a biomedical point of view. In the medical domain, these patterns are useful for understanding various diseases, and aid in more accurate diagnosis, prognosis, treatment planning, as well as drug discovery.
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Keywords
Journal Title bmc bioinformatics
Publication Year Start
%A Liu, Junwan; Li, Zhoujun; Hu, Xiaohua; Chen, Yiming
%T Biclustering of microarray data with MOSPO based on crowding distance.
%J BMC bioinformatics, vol. 10 Suppl 4, p. S9
%D 04/2009
%V 10 Suppl 4
%M eng
%B High-throughput microarray technologies have generated and accumulated massive amounts of gene expression datasets that contain expression levels of thousands of genes under hundreds of different experimental conditions. The microarray datasets are usually presented in 2D matrices, where rows represent genes and columns represent experimental conditions. The analysis of such datasets can discover local structures composed by sets of genes that show coherent expression patterns under subsets of experimental conditions. It leads to the development of sophisticated algorithms capable of extracting novel and useful knowledge from a biomedical point of view. In the medical domain, these patterns are useful for understanding various diseases, and aid in more accurate diagnosis, prognosis, treatment planning, as well as drug discovery.
%K Algorithms, Cluster Analysis, Databases, Genetic, Gene Expression Profiling, Oligonucleotide Array Sequence Analysis
%P S9
%Y 10.1186/1471-2105-10-S4-S9
%W PHY
%G AUTHOR
%R 2009.......10....0L

@Article{Liu2009,
author="Liu, Junwan
and Li, Zhoujun
and Hu, Xiaohua
and Chen, Yiming",
title="Biclustering of microarray data with MOSPO based on crowding distance.",
journal="BMC bioinformatics",
year="2009",
month="Apr",
day="29",
volume="10 Suppl 4",
pages="S9",
keywords="Algorithms",
keywords="Cluster Analysis",
keywords="Databases, Genetic",
keywords="Gene Expression Profiling",
keywords="Oligonucleotide Array Sequence Analysis",
abstract="High-throughput microarray technologies have generated and accumulated massive amounts of gene expression datasets that contain expression levels of thousands of genes under hundreds of different experimental conditions. The microarray datasets are usually presented in 2D matrices, where rows represent genes and columns represent experimental conditions. The analysis of such datasets can discover local structures composed by sets of genes that show coherent expression patterns under subsets of experimental conditions. It leads to the development of sophisticated algorithms capable of extracting novel and useful knowledge from a biomedical point of view. In the medical domain, these patterns are useful for understanding various diseases, and aid in more accurate diagnosis, prognosis, treatment planning, as well as drug discovery.",
issn="1471-2105",
doi="10.1186/1471-2105-10-S4-S9",
url="http://www.ncbi.nlm.nih.gov/pubmed/19426457",
language="eng"
}

%0 Journal Article
%T Biclustering of microarray data with MOSPO based on crowding distance.
%A Liu, Junwan
%A Li, Zhoujun
%A Hu, Xiaohua
%A Chen, Yiming
%J BMC bioinformatics
%D 2009
%8 April 29
%V 10 Suppl 4
%@ 1471-2105
%G eng
%F Liu2009
%X High-throughput microarray technologies have generated and accumulated massive amounts of gene expression datasets that contain expression levels of thousands of genes under hundreds of different experimental conditions. The microarray datasets are usually presented in 2D matrices, where rows represent genes and columns represent experimental conditions. The analysis of such datasets can discover local structures composed by sets of genes that show coherent expression patterns under subsets of experimental conditions. It leads to the development of sophisticated algorithms capable of extracting novel and useful knowledge from a biomedical point of view. In the medical domain, these patterns are useful for understanding various diseases, and aid in more accurate diagnosis, prognosis, treatment planning, as well as drug discovery.
%K Algorithms
%K Cluster Analysis
%K Databases, Genetic
%K Gene Expression Profiling
%K Oligonucleotide Array Sequence Analysis
%U http://dx.doi.org/10.1186/1471-2105-10-S4-S9
%U http://www.ncbi.nlm.nih.gov/pubmed/19426457
%P S9

PT Journal
AU Liu, J
   Li, Z
   Hu, X
   Chen, Y
TI Biclustering of microarray data with MOSPO based on crowding distance.
SO BMC bioinformatics
JI BMC Bioinformatics
PD 04
PY 2009
BP S9
VL 10 Suppl 4
DI 10.1186/1471-2105-10-S4-S9
LA eng
DE Algorithms; Cluster Analysis; Databases, Genetic; Gene Expression Profiling; Oligonucleotide Array Sequence Analysis
AB High-throughput microarray technologies have generated and accumulated massive amounts of gene expression datasets that contain expression levels of thousands of genes under hundreds of different experimental conditions. The microarray datasets are usually presented in 2D matrices, where rows represent genes and columns represent experimental conditions. The analysis of such datasets can discover local structures composed by sets of genes that show coherent expression patterns under subsets of experimental conditions. It leads to the development of sophisticated algorithms capable of extracting novel and useful knowledge from a biomedical point of view. In the medical domain, these patterns are useful for understanding various diseases, and aid in more accurate diagnosis, prognosis, treatment planning, as well as drug discovery.
ER

PMID- 19426457
OWN - NLM
STAT- MEDLINE
DA  - 20090511
DCOM- 20090618
LR  - 20141209
IS  - 1471-2105 (Electronic)
IS  - 1471-2105 (Linking)
VI  - 10 Suppl 4
DP  - 2009
TI  - Biclustering of microarray data with MOSPO based on crowding distance.
PG  - S9
LID - 10.1186/1471-2105-10-S4-S9 [doi]
AB  - BACKGROUND: High-throughput microarray technologies have generated and
      accumulated massive amounts of gene expression datasets that contain expression
      levels of thousands of genes under hundreds of different experimental conditions.
      The microarray datasets are usually presented in 2D matrices, where rows
      represent genes and columns represent experimental conditions. The analysis of
      such datasets can discover local structures composed by sets of genes that show
      coherent expression patterns under subsets of experimental conditions. It leads
      to the development of sophisticated algorithms capable of extracting novel and
      useful knowledge from a biomedical point of view. In the medical domain, these
      patterns are useful for understanding various diseases, and aid in more accurate 
      diagnosis, prognosis, treatment planning, as well as drug discovery. RESULTS: In 
      this work we present the CMOPSOB (Crowding distance based Multi-objective
      Particle Swarm Optimization Biclustering), a novel clustering approach for
      microarray datasets to cluster genes and conditions highly related in
      sub-portions of the microarray data. The objective of biclustering is to find
      sub-matrices, i.e. maximal subgroups of genes and subgroups of conditions where
      the genes exhibit highly correlated activities over a subset of conditions. Since
      these objectives are mutually conflicting, they become suitable candidates for
      multi-objective modelling. Our approach CMOPSOB is based on a heuristic search
      technique, multi-objective particle swarm optimization, which simulates the
      movements of a flock of birds which aim to find food. In the meantime, the
      nearest neighbour search strategies based on crowding distance and -dominance can
      rapidly converge to the Pareto front and guarantee diversity of solutions. We
      compare the potential of this methodology with other biclustering algorithms by
      analyzing two common and public datasets of gene expression profiles. In all
      cases our method can find localized structures related to sets of genes that show
      consistent expression patterns across subsets of experimental conditions. The
      mined patterns present a significant biological relevance in terms of related
      biological processes, components and molecular functions in a species-independent
      manner. CONCLUSION: The proposed CMOPSOB algorithm is successfully applied to
      biclustering of microarray dataset. It achieves a good diversity in the obtained 
      Pareto front, and rapid convergence. Therefore, it is a useful tool to analyze
      large microarray datasets.
FAU - Liu, Junwan
AU  - Liu J
AD  - School of Computer, National University of Deference Technology, Changsha, PR
      China. [email protected]
FAU - Li, Zhoujun
AU  - Li Z
FAU - Hu, Xiaohua
AU  - Hu X
FAU - Chen, Yiming
AU  - Chen Y
LA  - eng
PT  - Journal Article
PT  - Research Support, Non-U.S. Gov't
DEP - 20090429
PL  - England
TA  - BMC Bioinformatics
JT  - BMC bioinformatics
JID - 100965194
SB  - IM
MH  - *Algorithms
MH  - Cluster Analysis
MH  - Databases, Genetic
MH  - Gene Expression Profiling/*methods
MH  - Oligonucleotide Array Sequence Analysis/*methods
PMC - PMC2681067
OID - NLM: PMC2681067
EDAT- 2009/05/14 09:00
MHDA- 2009/06/19 09:00
CRDT- 2009/05/12 09:00
PHST- 2009/04/29 [aheadofprint]
AID - 1471-2105-10-S4-S9 [pii]
AID - 10.1186/1471-2105-10-S4-S9 [doi]
PST - epublish
SO  - BMC Bioinformatics. 2009 Apr 29;10 Suppl 4:S9. doi: 10.1186/1471-2105-10-S4-S9.
TY  - JOUR
AU  - Liu, Junwan
AU  - Li, Zhoujun
AU  - Hu, Xiaohua
AU  - Chen, Yiming
PY  - 2009/04/29
TI  - Biclustering of microarray data with MOSPO based on crowding distance.
T2  - BMC Bioinformatics
JO  - BMC bioinformatics
SP  - S9
VL  - 10 Suppl 4
KW  - Algorithms
KW  - Cluster Analysis
KW  - Databases, Genetic
KW  - Gene Expression Profiling
KW  - Oligonucleotide Array Sequence Analysis
N2  - High-throughput microarray technologies have generated and accumulated massive amounts of gene expression datasets that contain expression levels of thousands of genes under hundreds of different experimental conditions. The microarray datasets are usually presented in 2D matrices, where rows represent genes and columns represent experimental conditions. The analysis of such datasets can discover local structures composed by sets of genes that show coherent expression patterns under subsets of experimental conditions. It leads to the development of sophisticated algorithms capable of extracting novel and useful knowledge from a biomedical point of view. In the medical domain, these patterns are useful for understanding various diseases, and aid in more accurate diagnosis, prognosis, treatment planning, as well as drug discovery.
SN  - 1471-2105
UR  - http://dx.doi.org/10.1186/1471-2105-10-S4-S9
UR  - http://www.ncbi.nlm.nih.gov/pubmed/19426457
ID  - Liu2009
ER  - 
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