Weighted Markov Chain Based Aggregation of Biomolecule Orderings

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.

[1]  A. Hatzigeorgiou,et al.  A guide through present computational approaches for the identification of mammalian microRNA targets , 2006, Nature Methods.

[2]  R. Giegerich,et al.  Fast and effective prediction of microRNA/target duplexes. , 2004, RNA.

[3]  Moni Naor,et al.  Rank aggregation methods for the Web , 2001, WWW '01.

[4]  M. Bittner,et al.  Human prostate cancer and benign prostatic hyperplasia: molecular dissection by gene expression profiling. , 2001, Cancer research.

[5]  C. Burge,et al.  Prediction of Mammalian MicroRNA Targets , 2003, Cell.

[6]  J. Welsh,et al.  Analysis of gene expression identifies candidate markers and pharmacological targets in prostate cancer. , 2001, Cancer research.

[7]  Tongbin Li,et al.  miRecords: an integrated resource for microRNA–target interactions , 2008, Nucleic Acids Res..

[8]  D. Bartel,et al.  MicroRNAs Modulate Hematopoietic Lineage Differentiation , 2004, Science.

[9]  Ruth Etzioni,et al.  Combining Results of Microarray Experiments: A Rank Aggregation Approach , 2006 .

[10]  H. Young,et al.  A Consistent Extension of Condorcet’s Election Principle , 1978 .

[11]  Xiaowei Wang,et al.  Sequence analysis Prediction of both conserved and nonconserved microRNA targets in animals , 2007 .

[12]  Sanghamitra Bandyopadhyay,et al.  TargetMiner: microRNA target prediction with systematic identification of tissue-specific negative examples , 2009, Bioinform..

[13]  S. Dhanasekaran,et al.  Delineation of prognostic biomarkers in prostate cancer , 2001, Nature.

[14]  H. Young.  Condorcet's Theory of Voting , 1988, American Political Science Review.

[15]  Susmita Datta,et al.  Finding common genes in multiple cancer types through meta-analysis of microarray experiments: a rank aggregation approach. , 2008, Genomics.

[16]  Vasyl Pihur,et al.  RankAggreg, an R package for weighted rank aggregation , 2009, BMC Bioinformatics.

[17]  Ronald Fagin,et al.  Comparing top k lists , 2003, SODA '03.

[18]  Leroy Hood,et al.  A molecular correlate to the Gleason grading system for prostate adenocarcinoma. , 2006, Proceedings of the National Academy of Sciences of the United States of America.

[19]  E. Lander,et al.  Gene expression correlates of clinical prostate cancer behavior. , 2002, Cancer cell.

[20]  K. Gunsalus,et al.  Combinatorial microRNA target predictions , 2005, Nature Genetics.

[21]  Alfred De Grazia,et al.  Mathematical Derivation of an Election System , 1953 .

[22]  F. Slack,et al.  Oncomirs — microRNAs with a role in cancer , 2006, Nature Reviews Cancer.

[23]  D. Bartel.  MicroRNAs Genomics, Biogenesis, Mechanism, and Function , 2004, Cell.

[24]  S. Gupta.  On Some Multiple Decision (Selection and Ranking) Rules , 1965 .

[25]  Vasyl Pihur,et al.  Weighted rank aggregation of cluster validation measures: a Monte Carlo cross-entropy approach , 2007, Bioinform..

[26]  Dang D. Long,et al.  Potent effect of target structure on microRNA function , 2007, Nature Structural &Molecular Biology.

[27]  Ujjwal Maulik,et al.  Development of the human cancer microRNA network , 2010 .

[28]  Julius Brennecke,et al.  Identification of Drosophila MicroRNA Targets , 2003, PLoS biology.

[29]  Anton J. Enright,et al.  Human MicroRNA Targets , 2004, PLoS biology.

[30]  Jie Ding,et al.  Integration of Ranked Lists via Cross Entropy Monte Carlo with Applications to mRNA and microRNA Studies , 2009, Biometrics.

[31]  S. Falcon,et al.  Combining Results of Microarray Experiments: A Rank Aggregation Approach , 2006, Statistical applications in genetics and molecular biology.

[32]  Michael Kertesz,et al.  The role of site accessibility in microRNA target recognition , 2007, Nature Genetics.

[33]  Ujjwal Maulik,et al.  A novel measure for evaluating an ordered list: application in microRNA target prediction , 2010 .

[34]  Y. Li,et al.  Incorporating structure to predict microRNA targets. , 2005, Proceedings of the National Academy of Sciences of the United States of America.

[35]  Vincent F. Melfi,et al.  Composite MicroRNA Target Predictions and Comparisons of Several Prediction Algorithms , 2006 .

[36]  K. Kosik,et al.  MicroRNA-21 is an antiapoptotic factor in human glioblastoma cells. , 2005, Cancer research.

[37]  Nectarios Koziris,et al.  DIANA-microT web server: elucidating microRNA functions through target prediction , 2009, Nucleic Acids Res..

[38]  M. Truchon,et al.  An Extension of the Concordet Criterion and Kemeny Orders , 1998 .