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2000
Volume 21, Issue 6
  • ISSN: 1570-1646
  • E-ISSN: 1875-6247

Abstract

Deep learning represents a sophisticated technological advancement that leverages large-scale datasets and intricate models for feature extraction and pattern recognition, finding extensive application in domains such as computer vision and natural language processing. In recent years, deep learning has exhibited considerable promise in the analysis of complex biological data. The integration of this technology not only accelerates the processing speed of protein-related data but also enhances the accuracy of protein predictions, thereby providing substantial support for research in both fundamental biology and applied biotechnology. Presently, deep learning is predominantly employed in applications including protein sequence analysis, three-dimensional structure prediction, functional annotation, and the construction of protein interaction networks. These applications significantly facilitate research in related fields. Despite the growing prevalence of deep learning in this domain, several challenges persist, including data scarcity, limited model interpretability, and computational complexity, which constrain further advancements. This manuscript presents a comprehensive review of the latest applications of deep learning in protein prediction, addressing the associated challenges and exploring future developmental directions. It seeks to offer systematic theoretical discussions and practical foundations for research in this area, thereby facilitating the ongoing advancement and innovation of deep learning technologies within protein studies.

© 2024 Bentham Science Publishers
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2025-03-28
2025-10-30

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Deep Learning for the Prediction of Protein Sequence, Structure, Function, and Interaction: Applications, Challenges, and Future Directions
Curr. Proteomics 21, 561 (2024); https://doi.org/10.2174/0115701646374018250227075247
/content/journals/cp/10.2174/0115701646374018250227075247
/content/journals/cp/10.2174/0115701646374018250227075247
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  • Article Type:     Review Article
Keyword(s): Deep learning; functional annotation; interaction networks; protein; sequence analysis; structure prediction

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