Employing Machine Learning Techniques to Detect Protein Function: A Survey, Experimental, and Empirical Evaluations

Research output: Contribution to journalArticlepeer-review

Abstract

This review article delves deeply into the various machine learning (ML) methods and algorithms employed in discerning protein functions. Each method discussed is assessed for its efficacy, limitations, potential improvements, and future prospects. We present an innovative hierarchical classification system that arranges algorithms into intricate categories and unique techniques. This taxonomy is based on a tri-level hierarchy, starting with the methodology category and narrowing down to specific techniques. Such a framework allows for a structured and comprehensive classification of algorithms, assisting researchers in understanding the interrelationships among diverse algorithms and techniques. The study incorporates both empirical and experimental evaluations to differentiate between the techniques. The empirical evaluation ranks the techniques based on four criteria. The experimental assessments rank: (1) individual techniques under the same methodology subcategory, (2) different sub-categories within the same category, and (3) the broad categories themselves. Integrating the innovative methodological classification, empirical findings, and experimental assessments, the article offers a well-rounded understanding of ML strategies in protein function identification. The paper also explores techniques for multi-task and multi-label detection of protein functions, in addition to focusing on single-task methods. Moreover, the paper sheds light on the future avenues of ML in protein function determination.

Original languageBritish English
Pages (from-to)1-20
Number of pages20
JournalIEEE/ACM Transactions on Computational Biology and Bioinformatics
DOIs
StateAccepted/In press - 2024

Keywords

  • Biological system modeling
  • Classification algorithms
  • Machine learning
  • Machine Learning
  • Neural Networks
  • Prediction algorithms
  • Protein engineering
  • Protein Function Predication
  • Protein Function prediction using ML
  • Proteins
  • Recurrent neural networks

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