Abstract
N6-methyladenosine (m6A) is one of the most abundant forms of RNA methylation modifications currently known. It involves 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 predictor based on GBDT and stacking learning 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 the GBDT algorithm for feature selection, a stacking model is constructed by combining seven basic classifiers. Compared with other state-of-the-art methods, the results show that M6A-GSMS can obtain excellent performance for identifying the m6A sites. The prediction accuracy of A.thaliana, D.melanogaster, M.musculus, S.cerevisiae and Human 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.
Communicated by Ramaswamy H. Sarma
Disclosure statement
No potential conflict of interest was reported by the author(s).
Research involving human participants and/or animals
This article does not contain any studies with human participants or animals performed by any of the authors.
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There was no human participant and consent was not required.