Searching for microRNA prostate cancer target genes
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Pathway analysis of microRNAs in mouse heart development
International Journal of Bioinformatics Research and Applications
A Bayesian decision fusion approach for microRNA target prediction
Proceedings of the First ACM International Conference on Bioinformatics and Computational Biology
miRNA target prediction method based on the combination of multiple algorithms
ICIC'10 Proceedings of the 6th international conference on Advanced intelligent computing theories and applications: intelligent computing
Research Article: Ranking of microRNA target prediction scores by Pareto front analysis
Computational Biology and Chemistry
Predicting miRNA-mediated gene silencing mode based on miRNA-target duplex features
Computers in Biology and Medicine
SA-REPC: sequence alignment with regular expression path constraint
LATA'10 Proceedings of the 4th international conference on Language and Automata Theory and Applications
Weighted Markov Chain Based Aggregation of Biomolecule Orderings
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Journal of Biomedical Informatics
Machine learning methods for predicting tumor response in lung cancer
Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery
Derivative scores from site accessibility and ranking of miRNA target predictions
International Journal of Bioinformatics Research and Applications
Hi-index | 3.84 |
Motivation: MicroRNAs (miRNAs) are involved in many diverse biological processes and they may potentially regulate the functions of thousands of genes. However, one major issue in miRNA studies is the lack of bioinformatics programs to accurately predict miRNA targets. Animal miRNAs have limited sequence complementarity to their gene targets, which makes it challenging to build target prediction models with high specificity. Results: Here we present a new miRNA target prediction program based on support vector machines (SVMs) and a large microarray training dataset. By systematically analyzing public microarray data, we have identified statistically significant features that are important to target downregulation. Heterogeneous prediction features have been non-linearly integrated in an SVM machine learning framework for the training of our target prediction model, MirTarget2. About half of the predicted miRNA target sites in human are not conserved in other organisms. Our prediction algorithm has been validated with independent experimental data for its improved performance on predicting a large number of miRNA down-regulated gene targets. Availability: All the predicted targets were imported into an online database miRDB, which is freely accessible at http://mirdb.org. Contact: xwang@radonc.wustl.edu Supplementary information: Supplementary data are available at Bioinformatics online.