Implementation of machine learning models to determine the appropriate model for protein function prediction

Authors

DOI:

https://doi.org/10.15587/1729-4061.2022.263270

Keywords:

protein function prediction, classification, neural networks, ProtCNN, bidirectional long short-term memory (BiLSTM)

Abstract

Predicting the function of proteins is a crucial part of genome annotation, which can help in solving a wide range of biological problems. Many methods are available to predict the functions of proteins. However, except for sequence, most features are difficult to obtain or are not available for many proteins, which limits their scope. In addition, the performance of sequence-based feature prediction methods is often lower than that of methods that involve multiple features, and protein feature prediction can be time-consuming. Recent advances in this field are associated with the development of machine learning, which shows great progress in solving the problem of predicting protein functions. Today, however, most protein sequences have the status of «uncharacterized» or «putative».

The need to assess the accuracy of identification of protein functions is an urgent task for machine learning approaches used to predict protein functions. In this study, the performance of two popular function prediction algorithms (ProtCNN and BiLSTM) was assessed from two perspectives and the procedures for building these models were described.

As a result of the study of Pfam families, ProtCNN achieves an accuracy rate of 0.988 % and bidirectional LSTM has an accuracy rate of 0.9506 %. The use of the Pfam dataset allowed increasing the classification accuracy due to the large training dataset. The quality of the prediction increases with a large amount of training data.

The study demonstrated that machine learning algorithms can be used as an effective tool for building protein function prediction models, in particular, the CNN network can be adapted as an accurate tool for annotating protein functions in the presence of large datasets.

Author Biographies

Yekaterina Golenko, S. Seifullin Kazakh Agrotechnical University

Master of Science in Engineering

Department of Information Systems

Aisulu  Ismailova, S. Seifullin Kazakh Agrotechnical University

PhD

Department of Information Systems

Anargul Shaushenova, S. Seifullin Kazakh Agrotechnical University

Candidate of Technical Sciences

Department of Information Systems

Zhazira Mutalova, Zhangir khan West Kazakhstan Agrarian Technical University

Master of Technical Sciences

Higher School of Information Technologies

Damir Dossalyanov, Narxoz University

PhD

Department of Public and Local Management

Aliya Ainagulova, S. Seifullin Kazakh Agrotechnical University

Candidate of Technical Sciences

Akgul Naizagarayeva, S. Seifullin Kazakh Agrotechnical University

Master of Science in Engineering

Department of Information Systems

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Implementation of machine learning models to determine the appropriate model for protein function prediction

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Published

2022-10-30

How to Cite

Golenko, Y.,  Ismailova, A., Shaushenova, A., Mutalova, Z., Dossalyanov, D., Ainagulova, A., & Naizagarayeva, A. (2022). Implementation of machine learning models to determine the appropriate model for protein function prediction . Eastern-European Journal of Enterprise Technologies, 5(4(119), 42–49. https://doi.org/10.15587/1729-4061.2022.263270

Issue

Vol. 5 No. 4(119) (2022): Mathematics and Cybernetics - applied aspects

Section

Mathematics and Cybernetics - applied aspects

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Copyright (c) 2022 Yekaterina Golenko, Aisulu  Ismailova, Anargul Shaushenova, Zhazira Mutalova, Damir Dossalyanov, Aliya Ainagulova, Akgul Naizagarayeva

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