{"doi":"10.3389/fgene.2024.1376486","title":"ACP-DRL: an anticancer peptides recognition method based on deep representation learning","abstract":"<jats:p>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.</jats:p>","journal":"Frontiers in Genetics","year":2024,"id":637373,"datarank":0.37273599746820013,"base_score":2.4849066497880004,"endowment":2.4849066497880004,"self_citation_contribution":0.37273599746820013,"citation_network_contribution":0.0,"self_endowment_contribution":0.37273599746820013,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":11,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":null,"is_data_producer":false,"deposit_databanks":null,"is_oa":false,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":null,"fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":667810,"name":"Chaoran Li","orcid":"0000-0001-8103-9596","position":1,"is_corresponding":false},{"id":1654933,"name":"Xinpu Yuan","orcid":null,"position":2,"is_corresponding":false},{"id":1654935,"name":"Qiangjian Zhang","orcid":null,"position":3,"is_corresponding":false},{"id":831967,"name":"Yi Liu","orcid":"0000-0002-7782-4548","position":4,"is_corresponding":false},{"id":557233,"name":"Yunping Zhu","orcid":"0000-0002-7320-7411","position":5,"is_corresponding":false},{"id":655692,"name":"Tao Chen","orcid":"0000-0002-6552-3457","position":6,"is_corresponding":false},{"id":494349,"name":"Xiaofang Xu","orcid":"0000-0001-9281-9221","position":0,"is_corresponding":false}],"reference_count":0,"raw_metadata":{"has_enrichment":true,"resolved":true,"title":"ACP-DRL: an anticancer peptides recognition method based on deep representation learning","abstract":"<jats:p>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.</jats:p>","is_dataset_classified":null,"base_score":2.4849066497880004,"endowment":2.4849066497880004,"datacite_reuse_total":0,"file_count":0,"downloads":0,"views":0,"has_version_chain":false,"is_dataset":false,"is_oa":false,"pmid":"38655048","pmcid":"PMC11035771","openalex_id":"https://openalex.org/W4394617362","authors":[],"funders":[],"total_grants":0,"fwci":1.8072,"citation_percentile":0.84706475,"influential_citations":0,"citation_trend":[{"year":2024,"count":4},{"year":2025,"count":5},{"year":2026,"count":2}],"oa_status":"gold","license":"cc-by","oa_locations":[{"url":"https://www.frontiersin.org/journals/genetics/articles/10.3389/fgene.2024.1376486/pdf","host_type":"journal"},{"url":"https://www.frontiersin.org/journals/genetics/articles/10.3389/fgene.2024.1376486/pdf","host_type":"publisher"},{"url":"https://www.frontiersin.org/articles/10.3389/fgene.2024.1376486/full","host_type":"publisher"},{"url":"https://doi.org/10.3389/fgene.2024.1376486","host_type":"journal"},{"url":"https://pubmed.ncbi.nlm.nih.gov/38655048","host_type":"repository"},{"url":"https://www.ncbi.nlm.nih.gov/pmc/articles/11035771","host_type":"repository"},{"url":"https://doaj.org/article/14a36167c64d423ea46c7dd16acaff95","host_type":"repository"},{"url":"https://pmc.ncbi.nlm.nih.gov/articles/PMC11035771/pdf/fgene-15-1376486.pdf","host_type":"repository"},{"url":"https://europepmc.org/articles/PMC11035771","host_type":"Europe_PMC"},{"url":"https://europepmc.org/articles/PMC11035771?pdf=render","host_type":"Europe_PMC"}],"fields_of_study":["Machine Learning in Bioinformatics","Chemical Synthesis and Analysis","vaccines and immunoinformatics approaches"],"mesh_terms":[],"keywords":["Representation (politics)","Deep learning","Artificial intelligence","Computer science","Domain (mathematical analysis)","Named-entity recognition","Sequence (biology)","Computational biology","Machine learning","Chemistry","Biochemistry","Biology","Mathematics","Engineering","Anticancer Peptides","Pre-training","Language Models","Bert","Deep Representation Learning","Self-supervised"],"sdg_mappings":[],"linked_datasets":[],"clinical_trials":[],"software_tools":[],"database_accessions":[],"source":"live","citation_network_status":"fetched"},"created_at":"2026-08-06T18:49:45.387278Z","pmid":null,"pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}