An intelligent computational model for prediction of promoters and their strength via natural language processing

In DNA, a promoter is an essential part of genes that controls the transcription of specific genes in a particular tissue or cells. The combination of RNA polymerase and a number of various proteins named "sigma-factors" can define the transcription start site (TSS) by inducing RNA holoenzyme. Further, Promoter is categorized into strong and weak promoters on the basis of promoter strength. Owing to exponential increase of RNA/DNA and protein samples in the post-genomic era, developing a simple and efficient sequential-based intelligent computational model for the discrimination of promoters is a challenging job. An intelligent computational model namely: 2L-iPSW(word2vec) was introduced for discrimination of promoters and their strength, in this regard. Machine learning and Deep learning algorithms in conjunction with natural language processing method i.e., "word2vec" are used. The proposed computational model 2L-iPSW(word2vec) achieved 91.42% of accuracy for 1st layer contains promoters and non-promoters which is 8.29% higher than the existing model, whereas 82.42% of accuracy for 2nd layer identifies strong promoter and weak promoter which is 11.22% advanced than the present model. Proposed 2L-iPSW(word2vec) model obtained efficient success rates than the present models in terms of all assessment metrics. It is thus greatly observed that the 2L-iPSW(word2vec) model will lead a useful tool for academic research on promoter identification.