Sequence Analysis to Predict Microrna Chemotherapy Resistance

Abstract

Recent findings suggest that microRNAs play important role in resistance to certain chemotherapies. The knowledge of what microRNAs are potentially resistant to given chemotherapies is therefore a crucial knowledge on drug design and therapy scheduling activities. In this study, we attempt to predict the list of microRNAs which are resistant to given drug using solely their mature sequence information. With this objective, we employ three common approaches for sequence classification in bioinformatics, i.e. pairwise, generative and discriminative models. The experimental results on a knowledge-driven dataset promote the use of pairwise models as a complementary tool in association studies for microRNAs and drugs.