{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,4]],"date-time":"2026-07-04T08:10:52Z","timestamp":1783152652183,"version":"3.54.6"},"reference-count":56,"publisher":"Oxford University Press (OUP)","issue":"3","license":[{"start":{"date-parts":[[2025,6,16]],"date-time":"2025-06-16T00:00:00Z","timestamp":1750032000000},"content-version":"vor","delay-in-days":46,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2020YFA0908700"],"award-info":[{"award-number":["2020YFA0908700"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62362062"],"award-info":[{"award-number":["62362062"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Anhui Higher Education Institutions of China","award":["2023AH051392"],"award-info":[{"award-number":["2023AH051392"]}]},{"name":"\u201cTianshan Elite\u201d Youth Talents\u2013Youth Science and Technology Innovation Talents of the Autonomous Region","award":["2023TSYCCX0104"],"award-info":[{"award-number":["2023TSYCCX0104"]}]},{"name":"Multimodal Major Chronic Disease Prevention and Control Science and Engineering Research Project","award":["MCD-2023-1-15"],"award-info":[{"award-number":["MCD-2023-1-15"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,5,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Identifying multifunctional therapeutic peptides (MFTP) is an important yet complex challenge in the realm of peptide recognition. Unlike monofunctional peptides, MFTP classification requires discerning fine-grained labeling information associated with amino acids, making it more intricate. Existing methods often ignore the nuanced semantics of these labels and fail to fully explore the interplay between peptide sequences and their labels. To address these issues, we propose a multilabel classification method named MultiPep-DLCL. This method uses a deep learning\u2013based model architecture to translate peptide sequences into sequence features by learning the local and global dependencies of multifunctional therapeutic peptide sequences. Additionally, the Label-Sequence Fusion Transformer is employed to efficiently learn high-quality label embeddings by mining effective information from peptide sequences. Finally, the correspondence between sequence features and label embeddings is strengthened through label-sequence contrastive learning. To tackle dataset imbalance, MultiPep-DLCL integrates a multilabel focal dice loss function alongside the traditional cross-entropy loss function. Experimental results demonstrate that the MultiPep-DLCL significantly outperforms existing methods in MFTP recognition.<\/jats:p>","DOI":"10.1093\/bib\/bbaf274","type":"journal-article","created":{"date-parts":[[2025,6,16]],"date-time":"2025-06-16T07:11:23Z","timestamp":1750057883000},"source":"Crossref","is-referenced-by-count":5,"title":["MultiPep-DLCL: recognition of multifunctional therapeutic peptides through deep learning with label-sequence contrastive learning"],"prefix":"10.1093","volume":"26","author":[{"given":"Ting","family":"Li","sequence":"first","affiliation":[{"name":"College of Mathematics and Systems Science , Xinjiang University, No. 777 Huarui Road, Shuimogou District, Urumqi, Xinjiang Uygur Autonomous Region 830046 ,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Henghui","family":"Fan","sequence":"additional","affiliation":[{"name":"Institutes of Physical Science and Information Technology , Anhui University, No. 111 Jiulong Road, Economic and Technological Development Zone, Hefei, Anhui Province 230601 ,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jianping","family":"Zhao","sequence":"additional","affiliation":[{"name":"College of Mathematics and Systems Science , Xinjiang University, No. 777 Huarui Road, Shuimogou District, Urumqi, Xinjiang Uygur Autonomous Region 830046 ,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaomei","family":"Yang","sequence":"additional","affiliation":[{"name":"College of Mathematics and Systems Science , Xinjiang University, No. 777 Huarui Road, Shuimogou District, Urumqi, Xinjiang Uygur Autonomous Region 830046 ,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Junfeng","family":"Xia","sequence":"additional","affiliation":[{"name":"Institutes of Physical Science and Information Technology , Anhui University, No. 111 Jiulong Road, Economic and Technological 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