Machine learning method for knowledge discovery experimented with otoneurological data
Computer Methods and Programs in Biomedicine
Searching for microRNA prostate cancer target genes
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
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
Mixed-membership naive Bayes models
Data Mining and Knowledge Discovery
Predicting miRNA-mediated gene silencing mode based on miRNA-target duplex features
Computers in Biology and Medicine
Predicting human miRNA target genes using a novel evolutionary methodology
SETN'12 Proceedings of the 7th Hellenic conference on Artificial Intelligence: theories and applications
Derivative scores from site accessibility and ranking of miRNA target predictions
International Journal of Bioinformatics Research and Applications
Computers in Biology and Medicine
Hi-index | 3.84 |
Motivation: Most computational methodologies for miRNA:mRNA target gene prediction use the seed segment of the miRNA and require cross-species sequence conservation in this region of the mRNA target. Methods that do not rely on conservation generate numbers of predictions, which are too large to validate. We describe a target prediction method (NBmiRTar) that does not require sequence conservation, using instead, machine learning by a naïve Bayes classifier. It generates a model from sequence and miRNA:mRNA duplex information from validated targets and artificially generated negative examples. Both the ‘seed’ and ‘out-seed’ segments of the miRNA:mRNA duplex are used for target identification. Results: The application of machine-learning techniques to the features we have used is a useful and general approach for microRNA target gene prediction. Our technique produces fewer false positive predictions and fewer target candidates to be tested. It exhibits higher sensitivity and specificity than algorithms that rely on conserved genomic regions to decrease false positive predictions. Availability: The NBmiRTar program is available at http://wotan.wistar.upenn.edu/NBmiRTar/ Contact: yousef@wistar.org Supplementary information: http://wotan.wistar.upenn.edu/NBmiRTar/