A deep learning ensemble for function prediction of hypothetical proteins from pathogenic bacterial species.

A deep learning ensemble for function prediction of hypothetical proteins from pathogenic bacterial species. Comput Biol Chem. 2019 Oct 19;83:107147 Authors: Mishra S, Rastogi YP, Jabin S, Kaur P, Amir M, Khatun S Abstract Protein function prediction is a crucial task in the post-genomics era due to their diverse irreplaceable roles in a biological system. Traditional methods involved cost-intensive and time-consuming molecular biology techniques but they proved to be ineffective after the outburst of sequencing data through the advent of cost-effective and advanced sequencing techniques. To manage the pace of annotation with that of data generation, there is a shift to computational approaches which are based on homology, sequence and structure-based features, protein-protein interaction networks, phylogenetic profiles, and physicochemical properties, etc. A combination of these features has proven to be promising for protein function prediction in terms of improving prediction accuracy. In the present work, we have employed a combination of features based on sequence, physicochemical property, subsequence and annotation features with a total of 9890 features extracted and/or calculated for 171,212 reviewed prokaryotic proteins of 9 bacterial phyla from UniProtKB, to train a supervised deep learning ensemble model with the aim to categorize a bacterial hypothetical/unreviewed protein's function into 1739 GO terms as functional class...
Source: Computational Biology and Chemistry - Category: Bioinformatics Authors: Tags: Comput Biol Chem Source Type: research