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Recent studies have indicated that microRNA (miRNA) may play an oncogenic or tumor suppressor role in human cancer. To study the regulatory role of miRNAs in tumorigenesis, an integrated platform has been set up to provide a user friendly interface for query. The main advantage of the present platform is that all the miRNA target genes' information and disease records are drawn from experimentally verified or high confidence records.

MicroRNAs (miRNAs) are major regulators of gene expression in plants and animals. They recognize their target messenger RNAs (mRNAs) by sequence complementarity and guide them to cleavage or translational arrest. So far, the prediction of plant miRNA-target pairs generally relies on the use of empirical parameters deduced from known miRNA-target interactions.

Contemporary molecular biology deals with wide and heterogeneous sets of measurements to model and understand underlying biological processes including complex diseases. Machine learning provides a frequent approach to build such models. However, the models built solely from measured data often suffer from overfitting, as the sample size is typically much smaller than the number of measured features. In this paper, we propose a random forest-based classifier that reduces this overfitting with the aid of prior knowledge in the form of a feature interaction network.

In this paper, we present miRTarVis+, a Web-based interactive visual analytics tool for miRNA target predictions and integrative analyses of multiple prediction results. Various microRNA (miRNA) target prediction algorithms have been developed to improve sequence-based miRNA target prediction by exploiting miRNA-mRNA expression profile data. There are also a few analytics tools to help researchers predict targets of miRNAs.