A Comprehensive Survey of Deep Learning Techniques in Protein Function Prediction

Protein function prediction is a major challenge in the field of bioinformatics which aims at predicting the functions performed by a known protein. Many protein data forms like protein sequences, protein structures, protein-protein interaction networks, and micro-array data representations are bein...

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Bibliographic Details
Published in:IEEE/ACM transactions on computational biology and bioinformatics 2023-05, Vol.20 (3), p.2291-2301
Main Authors: Dhanuka, Richa, Singh, Jyoti Prakash, Tripathi, Anushree
Format: Article
Language:English
Subjects:
Amino Acid Sequence
Bioinformatics
Computational Biology - methods
Computational modeling
Deep Learning
Feature extraction
Neural Networks, Computer
Performance prediction
Prediction models
Predictive models
protein function prediction
Protein interaction
Protein sequence
Proteins
Proteins - chemistry
Surveys
Training
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Summary:Protein function prediction is a major challenge in the field of bioinformatics which aims at predicting the functions performed by a known protein. Many protein data forms like protein sequences, protein structures, protein-protein interaction networks, and micro-array data representations are being used to predict functions. During the past few decades, abundant protein sequence data has been generated using high throughput techniques making them a suitable candidate for predicting protein functions using deep learning techniques. Many such advanced techniques have been proposed so far. It becomes necessary to comprehend all these works in a survey to provide a systematic view of all the techniques along with the chronology in which the techniques have advanced. This survey provides comprehensive details of the latest methodologies, their pros and cons as well as predictive accuracy, and a new direction in terms of interpretability of the predictive models needed to be ventured by protein function prediction systems.
ISSN:1545-5963
1557-9964
DOI:10.1109/TCBB.2023.3247634