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Empirical Evidence of the Applicability of Functional Clustering through Gene Expression Classification
May-June 2012 (vol. 9 no. 3)
pp. 788-798
ASCII Text
x 
 
J. Klema, M. Krejnik, "Empirical Evidence of the Applicability of Functional Clustering through Gene Expression Classification," IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 9, no. 3, pp. 788-798, May-June, 2012.
 
 
BibTex
x
 
@article{ 10.1109/TCBB.2012.23,
author = {J. Klema and M. Krejnik},
title = {Empirical Evidence of the Applicability of Functional Clustering through Gene Expression Classification},
journal ={IEEE/ACM Transactions on Computational Biology and Bioinformatics},
volume = {9},
number = {3},
issn = {1545-5963},
year = {2012},
pages = {788-798},
doi = {http://doi.ieeecomputersociety.org/10.1109/TCBB.2012.23},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - JOUR
JO - IEEE/ACM Transactions on Computational Biology and Bioinformatics
TI - Empirical Evidence of the Applicability of Functional Clustering through Gene Expression Classification
IS - 3
SN - 1545-5963
SP788
EP798
EPD - 788-798
A1 - J. Klema,
A1 - M. Krejnik,
PY - 2012
KW - learning (artificial intelligence)
KW - bioinformatics
KW - data analysis
KW - feature extraction
KW - genetics
KW - biological sample
KW - empirical evidence
KW - functional clustering
KW - gene expression classification
KW - biological knowledge
KW - gene-gene interactions
KW - gene expression data
KW - functionally related gene
KW - gene clusters
KW - classifier learning
KW - benchmark data sets
KW - gene expression clustering
KW - feature extraction technique
KW - Clustering algorithms
KW - Bioinformatics
KW - Algorithm design and analysis
KW - Gene expression
KW - Feature extraction
KW - Partitioning algorithms
KW - classification.
KW - Biological prior knowledge
KW - gene expression
KW - gene set analysis
KW - clustering
KW - feature extraction
VL - 9
JA - IEEE/ACM Transactions on Computational Biology and Bioinformatics
ER -
 
J. Klema, Dept. of Cybern., Czech Tech. Univ. in Prague, Prague, Czech Republic
M. Krejnik, Dept. of Cybern., Czech Tech. Univ. in Prague, Prague, Czech Republic
The availability of a great range of prior biological knowledge about the roles and functions of genes and gene-gene interactions allows us to simplify the analysis of gene expression data to make it more robust, compact, and interpretable. Here, we objectively analyze the applicability of functional clustering for the identification of groups of functionally related genes. The analysis is performed in terms of gene expression classification and uses predictive accuracy as an unbiased performance measure. Features of biological samples that originally corresponded to genes are replaced by features that correspond to the centroids of the gene clusters and are then used for classifier learning. Using 10 benchmark data sets, we demonstrate that functional clustering significantly outperforms random clustering without biological relevance. We also show that functional clustering performs comparably to gene expression clustering, which groups genes according to the similarity of their expression profiles. Finally, the suitability of functional clustering as a feature extraction technique is evaluated and discussed.

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Index Terms:
learning (artificial intelligence),bioinformatics,data analysis,feature extraction,genetics,biological sample,empirical evidence,functional clustering,gene expression classification,biological knowledge,gene-gene interactions,gene expression data,functionally related gene,gene clusters,classifier learning,benchmark data sets,gene expression clustering,feature extraction technique,Clustering algorithms,Bioinformatics,Algorithm design and analysis,Gene expression,Feature extraction,Partitioning algorithms,classification.,Biological prior knowledge,gene expression,gene set analysis,clustering,feature extraction
Citation:
J. Klema, M. Krejnik, "Empirical Evidence of the Applicability of Functional Clustering through Gene Expression Classification," IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 9, no. 3, pp. 788-798, May-June 2012, doi:10.1109/TCBB.2012.23
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