2024
DOI: 10.3389/fgene.2024.1376486
|View full text |Cite
|
Sign up to set email alerts
|

ACP-DRL: an anticancer peptides recognition method based on deep representation learning

Xiaofang Xu,
Chaoran Li,
Xinpu Yuan
et al.

Abstract: 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 lea… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
1

Citation Types

0
1
0

Year Published

2024
2024
2025
2025

Publication Types

Select...
3

Relationship

0
3

Authors

Journals

citations
Cited by 3 publications
(1 citation statement)
references
References 34 publications
0
1
0
Order By: Relevance
“…Taking the advantage of large language models, a peptide language model OPP (OntoProtein within Peptides) has been developed to serve the purpose of peptide sequence representation. Integrating OPP with Bi-LSTM, the ACP-DRL model 29 effectively removes constraints on sequence length and the dependence on manual feature engineering, resulting in competitive performance compared to existing methods.…”
Section: ■ Introductionmentioning
confidence: 99%
“…Taking the advantage of large language models, a peptide language model OPP (OntoProtein within Peptides) has been developed to serve the purpose of peptide sequence representation. Integrating OPP with Bi-LSTM, the ACP-DRL model 29 effectively removes constraints on sequence length and the dependence on manual feature engineering, resulting in competitive performance compared to existing methods.…”
Section: ■ Introductionmentioning
confidence: 99%