Prediction of N6-methyladenosine sites using convolution neural network model based on distributed feature representations

N-6-methyladenosine (m(6)A) is a well-studied and most common interior messenger RNA (mRNA) modification that plays an important function in cell development. N(6)A is found in all kingdoms of life and many other cellular processes such as RNA splicing, immune tolerance, regulatory functions, RNA processing, and cancer. Despite the crucial role of m(6)A in cells, it was targeted computationally, but unfortunately, the obtained results were unsatisfactory. It is imperative to develop an efficient computational model that can truly represent m(6)A sites. In this regard, an intelligent and highly discriminative computational model namely: m6A-word2vec is introduced for the discrimination of m(6)A sites. Here, a concept of natural language processing in the form of word2vec is used to represent the motif of the target class automatically. These motifs (numerical descriptors) are automatically targeted from the human genome without any clear definition. Further, the extracted feature space is then forwarded to the convolution neural network model as input for prediction. The developed computational model obtained 83.17%, 92.69%, and 90.50% accuracy for benchmark datasets S-1, S-2, and S-3, respectively, using a 10-fold cross-validation test. The predictive outcomes validate that the developed intelligent computational model showed better performance compared to existing computational models. It is thus greatly estimated that the introduced computational model m6A-word2vec may be a supportive and practical tool for elementary and pharmaceutical research such as in drug design along with academia. (C) 2020 Elsevier Ltd. All rights reserved.