Computational prediction of species-specific yeast DNA replication origin via iterative feature representation.

Computational prediction of species-specific yeast DNA replication origin via iterative feature representation. Brief Bioinform. 2020 Nov 25;: Authors: Manavalan B, Basith S, Shin TH, Lee G Abstract Deoxyribonucleic acid replication is one of the most crucial tasks taking place in the cell, and it has to be precisely regulated. This process is initiated in the replication origins (ORIs), and thus it is essential to identify such sites for a deeper understanding of the cellular processes and functions related to the regulation of gene expression. Considering the important tasks performed by ORIs, several experimental and computational approaches have been developed in the prediction of such sites. However, existing computational predictors for ORIs have certain curbs, such as building only single-feature encoding models, limited systematic feature engineering efforts and failure to validate model robustness. Hence, we developed a novel species-specific yeast predictor called yORIpred that accurately identify ORIs in the yeast genomes. To develop yORIpred, we first constructed optimal 40 baseline models by exploring eight different sequence-based encodings and five different machine learning classifiers. Subsequently, the predicted probability of 40 models was considered as the novel feature vector and carried out iterative feature learning approach independently using five different classifiers. Our systematic analysis revealed that t...
Source: Briefings in Bioinformatics - Category: Bioinformatics Authors: Tags: Brief Bioinform Source Type: research