A Machine Learning Approach for the Integration of miRNA-Target Predictions

Although several computational methods have been developed for predicting interactions between miRNA and target genes, there are substantial differences in the achieved results. For this reason, machine learning approaches are widely used for integrating the predictions obtained from different tools. In this work we adopt a method, called M3GP, which relies on a genetic programming approach, to classify results from three tools: miRanda, TargetScan, and RNAhybrid. Such algorithm is highly parallelizable and its adoption provides great advantages while handling problems involving big datasets, since it is independent from the implementation and from the architecture on which it is executed. More precisely, we apply this technique for the classification of the achieved miRNA target predictions and we compare its results with those obtained with other classifiers.

[1]  Peter J. Angeline,et al.  Massively Parallel Genetic Programming , 1996 .

[2]  C. Croce,et al.  A microRNA expression signature of human solid tumors defines cancer gene targets , 2006, Proceedings of the National Academy of Sciences of the United States of America.

[3]  Yves A. Lussier,et al.  ExprTarget: An Integrative Approach to Predicting Human MicroRNA Targets , 2010, PloS one.

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

[5]  Juan Julián Merelo Guervós,et al.  The EvoSpace Model for Pool-Based Evolutionary Algorithms , 2015, Journal of Grid Computing.

[6]  C. Croce,et al.  Human microRNA genes are frequently located at fragile sites and genomic regions involved in cancers. , 2004, Proceedings of the National Academy of Sciences of the United States of America.

[7]  C. Burge,et al.  Most mammalian mRNAs are conserved targets of microRNAs. , 2008, Genome research.

[8]  V. Ambros,et al.  The C. elegans heterochronic gene lin-4 encodes small RNAs with antisense complementarity to lin-14 , 1993, Cell.

[9]  Hsien-Da Huang,et al.  miRTarBase update 2014: an information resource for experimentally validated miRNA-target interactions , 2013, Nucleic Acids Res..

[10]  Ivan Merelli,et al.  myMIR: a genome-wide microRNA targets identification and annotation tool , 2011, Briefings Bioinform..

[11]  Andrzej Zielezinski,et al.  mirEX: a platform for comparative exploration of plant pri-miRNA expression data , 2011, Nucleic Acids Res..

[12]  Dirk Sudholt,et al.  Analysis of Speedups in Parallel Evolutionary Algorithms for Combinatorial Optimization - (Extended Abstract) , 2011, ISAAC.

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

[14]  Alex E. Lash,et al.  Gene Expression Omnibus: NCBI gene expression and hybridization array data repository , 2002, Nucleic Acids Res..

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

[16]  Yufei Huang,et al.  Survey of Computational Algorithms for MicroRNA Target Prediction , 2009, Current genomics.

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

[18]  Aybar C. Acar,et al.  mESAdb: microRNA Expression and Sequence Analysis Database , 2010, Nucleic Acids Res..

[19]  Luis Muñoz,et al.  M3GP - Multiclass Classification with GP , 2015, EuroGP.

[20]  Sanghyuk Lee,et al.  miRGator: an integrated system for functional annotation of microRNAs , 2007, Nucleic Acids Res..

[21]  Enrique Alba,et al.  A survey of parallel distributed genetic algorithms , 1999, Complex..

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

[23]  Anton J. Enright,et al.  MicroRNA targets in Drosophila , 2003, Genome Biology.

[24]  Dirk Sudholt,et al.  Analysis of speedups in parallel evolutionary algorithms and (1+λ) EAs for combinatorial optimization , 2014, Theor. Comput. Sci..

[25]  Leonardo Vanneschi,et al.  A Multi-dimensional Genetic Programming Approach for Multi-class Classification Problems , 2014, EuroGP.

[26]  Gabriele Sales,et al.  MAGIA, a web-based tool for miRNA and Genes Integrated Analysis , 2010, Nucleic Acids Res..