title: MultiMiTar: a novel multi objective optimization based miRNA-target prediction method.
abstract: BACKGROUND
  Machine learning based miRNA-target prediction algorithms often fail to obtain a balanced prediction accuracy in terms of both sensitivity and specificity due to lack of the gold standard of negative examples, miRNA-targeting site context specific relevant features and efficient feature selection process. Moreover, all the sequence, structure and machine learning based algorithms are unable to distribute the true positive predictions preferentially at the top of the ranked list; hence the algorithms become unreliable to the biologists. In addition, these algorithms fail to obtain considerable combination of precision and recall for the target transcripts that are translationally repressed at protein level.
  
  
  RESULTS
  In the proposed article, we introduce an efficient miRNA-target prediction system MultiMiTar, a Support Vector Machine (SVM) based classifier integrated with a multiobjective metaheuristic based feature selection technique. The robust performance of the proposed method is mainly the result of using high quality negative examples and selection of biologically relevant miRNA-targeting site context specific features. The features are selected by using a novel feature selection technique AMOSA-SVM, that integrates the multi objective optimization technique Archived Multi-Objective Simulated Annealing (AMOSA) and SVM.
  
  
  CONCLUSIONS
  MultiMiTar is found to achieve much higher Matthew's correlation coefficient (MCC) of 0.583 and average class-wise accuracy (ACA) of 0.8 compared to the others target prediction methods for a completely independent test data set. The obtained MCC and ACA values of these algorithms range from -0.269 to 0.155 and 0.321 to 0.582, respectively. Moreover, it shows a more balanced result in terms of precision and sensitivity (recall) for the translationally repressed data set as compared to all the other existing methods. An important aspect is that the true positive predictions are distributed preferentially at the top of the ranked list that makes MultiMiTar reliable for the biologists. MultiMiTar is now available as an online tool at www.isical.ac.in/~bioinfo_miu/multimitar.htm. MultiMiTar software can be downloaded from www.isical.ac.in/~bioinfo_miu/multimitar-download.htm.
authors.affiliation: Machine Intelligence Unit, Indian Statistical Institute, Kolkata, West Bengal, India.
authors.forename: Ramkrishna
authors.initials: R
authors.lastname: Mitra
authors.forename: Sanghamitra
authors.initials: S
authors.lastname: Bandyopadhyay
mesh_terms: D000465:Algorithms; D019295:Computational Biology / Q000379:methods*; D015536:Down-Regulation / Q000235:genetics; D006367:HeLa Cells; D006801:Humans; D035683:MicroRNAs / Q000235:genetics* / Q000378:metabolism*; D011506:Proteins / Q000235:genetics; D060388:Support Vector Machine
chemical_list: D035683:MicroRNAs; D011506:Proteins