M6A-GSMS

N6-methyladenosine (m6A) is one of the most abundant forms of RNA methylation modifications currently known. It involves in a wide range of biological processes including degradation, stability, alternative splicing, etc. Therefore, the development of convenient and efficient m6A prediction technologies are urgent. In this work, a novel stacking learning-based predictor is developed to identify m6A sites, which is called M6A-GSMS. To achieve accurate prediction, we explore RNA sequence information from four aspects: correlation, structure, physicochemical properties and pseudo ribonucleic acid composition. After using GBDT algorithm for feature selection, a stacking model is constructed based on seven basic classifiers. Compared with other state-of-the-art methods, the results show that M6A-GSMS is able to obtain excellent performance for m6A site identification. The prediction accuracy of A.thaliana, D.Melanogaster, M.Musculus, S.cerevisiae and Human sequences reaches 88.4%, 60.8%, 80.5%, 92.4% and 61.8%, respectively. This method provides an effective prediction for the investigation of m6A sites. In addition, all the datasets and codes are currently available at https://github.com/Wang-Jinyue/M6A-GSMS.