title: A Bayesian decision fusion approach for microRNA target prediction.
abstract: MicroRNAs (miRNAs) are 19-25 nucleotides non-coding RNAs known to have important post-transcriptional regulatory functions. The computational target prediction algorithm is vital to effective experimental testing. However, since different existing algorithms rely on different features and classifiers, there is a poor agreement among the results of different algorithms. To benefit from the advantages of different algorithms, we proposed an algorithm called BCmicrO that combines the prediction of different algorithms with Bayesian Network. BCmicrO was evaluated using the training data and the proteomic data. The results show that BCmicrO improves both the sensitivity and the specificity of each individual algorithm. All the related materials including genome-wide prediction of human targets and a web-based tool are available at http://compgenomics.utsa.edu/gene/gene_1.php.
authors.affiliation: Department of Electrical and Computer Engineering, University of Texas at San Antonio, San Antonio, Texas 78249, USA.
authors.forename: Dong
authors.initials: D
authors.lastname: Yue
authors.forename: Maozu
authors.initials: M
authors.lastname: Guo
authors.forename: Yidong
authors.initials: Y
authors.lastname: Chen
authors.forename: Yufei
authors.initials: Y
authors.lastname: Huang
mesh_terms: D000465:Algorithms; D000818:Animals; D001499:Bayes Theorem; D015894:Genome, Human; D006801:Humans; D020407:Internet; D051379:Mice; D035683:MicroRNAs / Q000378:metabolism*; D011506:Proteins / Q000235:genetics / Q000378:metabolism; D040901:Proteomics; D012372:ROC Curve; D051381:Rats; D014584:User-Computer Interface
chemical_list: D035683:MicroRNAs; D011506:Proteins