Machine Learning
Machine Learning
Kernel design for RNA classification using Support Vector Machines
International Journal of Data Mining and Bioinformatics
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
In silico prediction of noncoding RNAs using supervised learning and feature ranking methods
International Journal of Bioinformatics Research and Applications
Computational Biology and Chemistry
Circular code motifs in transfer and 16S ribosomal RNAs: A possible translation code in genes
Computational Biology and Chemistry
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RNA tertiary interactions or tertiary motifs are conserved structural patterns formed by pairwise interactions between nucleotides. They include base-pairing, base-stacking, and base-phosphate interactions. A-minor motifs are the most common tertiary interactions in the large ribosomal subunit. The A-minor motif is a nucleotide triple in which minor groove edges of an adenine base are inserted into the minor groove of neighboring helices, leading to interaction with a stabilizing base pair. We propose here novel features for identifying and predicting A-minor motifs in a given three-dimensional RNA molecule. By utilizing the features together with machine learning algorithms including random forests and support vector machines, we show experimentally that our approach is capable of predicting A-minor motifs in the given RNA molecule effectively, demonstrating the usefulness of the proposed approach. The techniques developed from this work will be useful for molecular biologists and biochemists to analyze RNA tertiary motifs, specifically A-minor interactions.