Open AccessA Neural Network-Based Multi-Label Classifier for Protein Function Prediction
Author(s) -
Shahab Tahzeeb,
Shehzad Hasan
Publication year - 2022
Publication title -
engineering, technology and applied science research/engineering, technology and applied science research
Language(s) - English
Resource type - Journals
eISSN - 2241-4487
pISSN - 1792-8036
DOI - 10.48084/etasr.4597
Subject(s) - computer science , artificial neural network , classifier (uml) , uniprot , protein function prediction , artificial intelligence , turnaround time , machine learning , function (biology) , gene ontology , protein function , set (abstract data type) , data mining , gene , biology , biochemistry , gene expression , evolutionary biology , programming language , operating system
Knowledge of the functions of proteins plays a vital role in gaining a deep insight into many biological studies. However, wet lab determination of protein function is prohibitively laborious, time-consuming, and costly. These challenges have created opportunities for automated prediction of protein functions, and many computational techniques have been explored. These techniques entail excessive computational resources and turnaround times. The current study compares the performance of various neural networks on predicting protein function. These networks were trained and tested on a large dataset of reviewed protein entries from nine bacterial phyla, obtained from the Universal Protein Resource Knowledgebase (UniProtKB). Each protein instance was associated with multiple terms of the molecular function of Gene Ontology (GO), making the problem a multilabel classification one. The results in this dataset showed the superior performance of single-layer neural networks having a modest number of neurons. Moreover, a useful set of features that can be deployed for efficient protein function prediction was discovered.