IBM Israel Research Seminars

I will be talking about two recent works for predict microRNA genes and targets as described below:

MicroRNA Gene Predictions
Most computational methodologies for microRNA gene prediction utilize techniques based on sequence conservation and/or structural similarity. In this study we describe a new technique, which is applicable across several species, for predicting microRNA genes. This technique is based on machine learning, using the Na·ve Bayes classifier. It automatically generates a model from the training data, which consists of sequence and structure information of known microRNAs from a variety of species. BayesMiRNAfind program is available at http://wotan.wistar.upenn.edu/miRNA

MicroRNA Gene Target Predictions
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 don’t rely on conservation generate numbers of predictions which are too large to validate. We describe a target prediction method, (NBmiRTar http://wotan.wistar.upenn.edu/NBmiRTar/). that does not require sequence conservation, using instead, machine learning by Na·ve 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.
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.