PROJECT TITLE :
YamiPred: A Novel Evolutionary Method for Predicting Pre-miRNAs and Selecting Relevant Features
MicroRNAs (miRNAs) are small non-coding RNAs, that play a important role in gene regulation. Predicting miRNA genes may be a difficult bioinformatics drawback and existing experimental and computational strategies fail to deal with it effectively. We have a tendency to developed YamiPred, an embedded classification method that combines the potency and robustness of support vector machines (SVM) with genetic algorithms (GA) for feature selection and parameters optimization. YamiPred was tested in a new and realistic human dataset and was compared with state-of-the-art computational intelligence approaches and therefore the prevalent SVM-primarily based tools for miRNA prediction. Experimental results indicate that YamiPred outperforms existing approaches in terms of accuracy and of geometric mean of sensitivity and specificity. The embedded feature choice component selects a compact feature subset that contributes to the performance optimization. Additional experimentation with this minimal feature subset has achieved very high classification performance and revealed the minimum variety of samples required for developing a strong predictor. YamiPred conjointly confirmed the vital role of commonly used features like entropy and enthalpy, and uncovered the significance of newly introduced features, such as %A-U mixture nucleotide frequency and positional entropy. The simplest model trained on human data has successfully predicted pre-miRNAs to alternative organisms together with the category of viruses.
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