ACP-DRL: an anticancer peptides recognition method based on deep representation learning.
In: Frontiers in genetics, Jg. 15 (2024-04-09), S. 1376486
Online
academicJournal
Zugriff:
Cancer, a significant global public health issue, resulted in about 10 million deaths in 2022. Anticancer peptides (ACPs), as a category of bioactive peptides, have emerged as a focal point in clinical cancer research due to their potential to inhibit tumor cell proliferation with minimal side effects. However, the recognition of ACPs through wet-lab experiments still faces challenges of low efficiency and high cost. Our work proposes a recognition method for ACPs named ACP-DRL based on deep representation learning, to address the challenges associated with the recognition of ACPs in wet-lab experiments. ACP-DRL marks initial exploration of integrating protein language models into ACPs recognition, employing in-domain further pre-training to enhance the development of deep representation learning. Simultaneously, it employs bidirectional long short-term memory networks to extract amino acid features from sequences. Consequently, ACP-DRL eliminates constraints on sequence length and the dependence on manual features, showcasing remarkable competitiveness in comparison with existing methods.
Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
(Copyright © 2024 Xu, Li, Yuan, Zhang, Liu, Zhu and Chen.)
Titel: |
ACP-DRL: an anticancer peptides recognition method based on deep representation learning.
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Autor/in / Beteiligte Person: | Xu, X ; Li, C ; Yuan, X ; Zhang, Q ; Liu, Y ; Zhu, Y ; Chen, T |
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Zeitschrift: | Frontiers in genetics, Jg. 15 (2024-04-09), S. 1376486 |
Veröffentlichung: | Lausanne : Frontiers Research Foundation., 2024 |
Medientyp: | academicJournal |
ISSN: | 1664-8021 (print) |
DOI: | 10.3389/fgene.2024.1376486 |
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