A Neural Network-Based Multi-Label Classifier for Protein Function Prediction

Authors

  • S. Tahzeeb Department of Computer and Information Systems Engineering, NED University of Engineering and Technology, Pakistan
  • S. Hasan Department of Computer and Information Systems Engineering, NED University of Engineering and Technology, Pakistan
Volume: 12 | Issue: 1 | Pages: 7974-7981 | February 2022 | https://doi.org/10.48084/etasr.4597

Abstract

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.

Keywords:

molecular function term, multi-label classification, neural network, protein function prediction, gene ontology

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How to Cite

[1]
S. Tahzeeb and S. Hasan, “A Neural Network-Based Multi-Label Classifier for Protein Function Prediction”, Eng. Technol. Appl. Sci. Res., vol. 12, no. 1, pp. 7974–7981, Feb. 2022.

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