Gene Ontology Capsule GAN: an improved architecture for protein function prediction

Abstract

Proteins are the core of all functions pertaining to living things. They consist of an extended amino acid chain folding into a three-dimensional shape that dictates their behavior. Currently, convolutional neural networks (CNNs) have been pivotal in predicting protein functions based on protein sequences. While it is a technology crucial to the niche, the computation cost and translational invariance associated with CNN make it impossible to detect spatial hierarchies between complex and simpler objects. Therefore, this research utilizes capsule networks to capture spatial information as opposed to CNNs. Since capsule networks focus on hierarchical links, they have a lot of potential for solving structural biology challenges. In comparison to the standard CNNs, our results exhibit an improvement in accuracy. Gene Ontology Capsule GAN (GOCAPGAN) achieved an F1 score of 82.6%, a precision score of 90.4% and recall score of 76.1%.

An informatic workflow for the enhanced annotation of excretory/secretory proteins of Haemonchus contortus

“… [34] ). Nonetheless, given the challenges associated with the annotation of “unknown” or “orphan” molecules, ML and structure-based tools (established recently; [89] , [90] , [91] , [92] ) appeared to provide a useful alternative to sequence-based methods, although often their performance had not been thoroughly evaluated individually or in combination [93] . To address this issue, we evaluated individual tools and optimised the cut-off values for select algorithms (cf.…”