Photosynthetic protein classification using genome neighborhood-based machine learning feature.

ABSTRACT: Identification of novel photosynthetic proteins is important for understanding and improving photosynthetic efficiency. Synergistically, genome neighborhood can provide additional useful information to identify photosynthetic proteins. We, therefore, expected that applying a computational approach, particularly machine learning (ML) with the genome neighborhood-based feature should facilitate the photosynthetic function assignment. Our results revealed a functional relationship between photosynthetic genes and their conserved neighboring genes observed by 'Phylo score', indicating their functions could be inferred from the genome neighborhood profile. Therefore, we created a new method for extracting patterns based on the genome neighborhood network (GNN) and applied them for the photosynt

SUBMITTER: Sangphukieo A 

PROVIDER: S-EPMC7189237 | biostudies-literature | 2020 Apr

REPOSITORIES: biostudies-literature