Prediction of Breast Cancer Using Ensemble Learning

RNA modification refers to the local structural changes or new chemical group additions in nucleotides. It has impact on some crucial biological activities and is also linked to several serious diseases, e.g. leukemia, breast cancer, zika virus and so on. That's why the identification of RNA modifications attains great concern. N1-methyladenosine (m1A) and N-6-methyladenosine (m(6)A) are two frequent modifications which occur at the adenosine site of RNA. So far, various methods have been developed to predict the modifications, e.g. iRNA-3typeA, RAM-ESVM etc. However, these methods can be improved further with the help of multiple feature selection approaches. In this paper, we have done extensive analysis on the effect of multiple feature selection methods and proposed a hybrid feature selection approach. This hybrid feature selection approach considers the common features that have been selected by Student's t-test, Kruskal-Wallis test and minimum redundancy maximum relevance (mRMR) method. Applying this approach with 10-fold cross-validation and support vector machine classifier, we have obtained 99.37% and 91.02% accuracy for m1A and m6A (Homo sapiens), and 89.97% and 98.17% accuracy for m1A and m6A (Mus musculus) respectively.