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Weighted Markov Chain Based Aggregation of Biomolecule Orderings
May-June 2012 (vol. 9 no. 3)
pp. 924-933
ASCII Text
x 
 
Debarka Sengupta, Ujjwal Maulik, Sanghamitra Bandyopadhyay, "Weighted Markov Chain Based Aggregation of Biomolecule Orderings," IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 9, no. 3, pp. 924-933, May-June, 2012.
 
 
BibTex
x
 
@article{ 10.1109/TCBB.2012.28,
author = {Debarka Sengupta and Ujjwal Maulik and Sanghamitra Bandyopadhyay},
title = {Weighted Markov Chain Based Aggregation of Biomolecule Orderings},
journal ={IEEE/ACM Transactions on Computational Biology and Bioinformatics},
volume = {9},
number = {3},
issn = {1545-5963},
year = {2012},
pages = {924-933},
doi = {http://doi.ieeecomputersociety.org/10.1109/TCBB.2012.28},
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 - Weighted Markov Chain Based Aggregation of Biomolecule Orderings
IS - 3
SN - 1545-5963
SP924
EP933
EPD - 924-933
A1 - Debarka Sengupta,
A1 - Ujjwal Maulik,
A1 - Sanghamitra Bandyopadhyay,
PY - 2012
KW - Rank aggregation
KW - Markov chain
KW - Kendall's tau
KW - microRNA
KW - genes
KW - ordering.
VL - 9
JA - IEEE/ACM Transactions on Computational Biology and Bioinformatics
ER -
 
Debarka Sengupta, Indian Statistical Institute, Kolkata
Ujjwal Maulik, Jadavpur University, Kolkata
Sanghamitra Bandyopadhyay, Indian Statistical Institute, Kolkata
The scope and effectiveness of Rank Aggregation (RA) have already been established in contemporary bioinformatics research. Rank aggregation helps in meta-analysis of putative results collected from different analytic or experimental sources. For example, we often receive considerably differing ranked lists of genes or microRNAs from various target prediction algorithms or microarray studies. Sometimes combining them all, in some sense, yields more effective ordering of the set of objects. Also, assigning a certain level of confidence to each source of ranking is a natural demand of aggregation. Assignment of weights to the sources of orderings can be performed by experts. Several rank aggregation approaches like those based on Markov Chains (MCs), evolutionary algorithms, etc., exist in the literature. Markov chains, in general, are faster than the evolutionary approaches. Unlike the evolutionary computing approaches Markov chains have not been used for weighted aggregation scenarios. This is because of the absence of a formal framework of Weighted Markov Chain (WMC). In this paper, we propose the use of a modified version of MC4 (one of the Markov chains proposed by Dwork et al., 2001), followed by the weighted analog of local Kemenization for performing rank aggregation, where the sources of rankings can be prioritized by an expert. Effectiveness of the weighted Markov chain approach over the very recently proposed Genetic Algorithm (GA) and Cross-Entropy Monte Carlo (MC) algorithm-based techniques, has been established for gene orderings from microarray analysis and orderings of predicted microRNA targets.

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Index Terms:
Rank aggregation, Markov chain, Kendall's tau, microRNA, genes, ordering.
Citation:
Debarka Sengupta, Ujjwal Maulik, Sanghamitra Bandyopadhyay, "Weighted Markov Chain Based Aggregation of Biomolecule Orderings," IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 9, no. 3, pp. 924-933, May-June 2012, doi:10.1109/TCBB.2012.28
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