miRNA Target Prediction Based on Gene Ontology

miRNAs have been regarded as the key regulator in post transcriptional modification involved in broad biological process. Identifying miRNA targets is one of the core challenges in studying miRNA function. Previous miRNA target prediction algorithms are mainly based on physical interaction mechanism such as sequence match and free energy. In our paper, we proposed SVM ensemble classifier based method to integrate functional information from Gene Ontology with sequence information. To supplement our method, we constructed comprehensive positive data and high quality negative data from micro-array data. Performance evaluation shows significant improvement of the proposed method in both prediction performance and the coverage of miRNA-mRNA pairs. Further analysis of the GO features used for prediction suggests they appropriately represent the functional information of miRNA target genes.

[1]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.

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

[3]  Fuhui Long,et al.  Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy , 2003, IEEE Transactions on Pattern Analysis and Machine Intelligence.

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

[5]  William Ritchie,et al.  mimiRNA: a microRNA expression profiler and classification resource designed to identify functional correlations between microRNAs and their targets , 2010, Bioinform..

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

[7]  L. Lim,et al.  MicroRNA targeting specificity in mammals: determinants beyond seed pairing. , 2007, Molecular cell.

[8]  Bernhard E. Boser,et al.  A training algorithm for optimal margin classifiers , 1992, COLT '92.

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

[10]  A. Pasquinelli.  MicroRNAs and their targets: recognition, regulation and an emerging reciprocal relationship , 2012, Nature Reviews Genetics.

[11]  Wei-Yin Loh,et al.  Classification and regression trees , 2011, WIREs Data Mining Knowl. Discov..

[12]  Norbert Gretz,et al.  miRWalk - Database: Prediction of possible miRNA binding sites by "walking" the genes of three genomes , 2011, J. Biomed. Informatics.