{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,13]],"date-time":"2026-02-13T23:26:07Z","timestamp":1771025167858,"version":"3.50.1"},"reference-count":49,"publisher":"Oxford University Press (OUP)","issue":"5","license":[{"start":{"date-parts":[[2022,8,21]],"date-time":"2022-08-21T00:00:00Z","timestamp":1661040000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"funder":[{"name":"Information Materials and Intelligent Sensing Laboratory of Anhui Province","award":["IMIS202105"],"award-info":[{"award-number":["IMIS202105"]}]},{"name":"Xinjiang Autonomous Region University Research Program","award":["XJEDU2019Y002"],"award-info":[{"award-number":["XJEDU2019Y002"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["U19A2064"],"award-info":[{"award-number":["U19A2064"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61873001"],"award-info":[{"award-number":["61873001"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,9,20]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Neuropeptides (NPs) are a particular class of informative substances in the immune system and physiological regulation. They play a crucial role in regulating physiological functions in various biological growth and developmental stages. In addition, NPs are crucial for developing new drugs for the treatment of neurological diseases. With the development of molecular biology techniques, some data-driven tools have emerged to predict NPs. However, it is necessary to improve the predictive performance of these tools for NPs. In this study, we developed a deep learning model (NeuroPred-CLQ) based on the temporal convolutional network (TCN) and multi-head attention mechanism to identify NPs effectively and translate the internal relationships of peptide sequences into numerical features by the Word2vec algorithm. The experimental results show that NeuroPred-CLQ learns data information effectively, achieving 93.6% accuracy and 98.8% AUC on the independent test set. The model has better performance in identifying NPs than the state-of-the-art predictors. Visualization of features using t-distribution random neighbor embedding shows that the NeuroPred-CLQ can clearly distinguish the positive NPs from the negative ones. We believe the NeuroPred-CLQ can facilitate drug development and clinical trial studies to treat neurological disorders.<\/jats:p>","DOI":"10.1093\/bib\/bbac319","type":"journal-article","created":{"date-parts":[[2022,8,22]],"date-time":"2022-08-22T00:17:46Z","timestamp":1661127466000},"source":"Crossref","is-referenced-by-count":28,"title":["NeuroPred-CLQ: incorporating deep temporal convolutional networks and multi-head attention mechanism to predict neuropeptides"],"prefix":"10.1093","volume":"23","author":[{"given":"Shouzhi","family":"Chen","sequence":"first","affiliation":[{"name":"School of Mathematics and System Science, Xinjiang University , Urumqi, China"}]},{"given":"Qing","family":"Li","sequence":"additional","affiliation":[{"name":"School of Mathematics and System Science, Xinjiang University , Urumqi, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8486-744X","authenticated-orcid":false,"given":"Jianping","family":"Zhao","sequence":"additional","affiliation":[{"name":"School of Mathematics and System Science, Xinjiang University , Urumqi, China"}]},{"given":"Yannan","family":"Bin","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Anhui University , Hefei, China"}]},{"given":"Chunhou","family":"Zheng","sequence":"additional","affiliation":[{"name":"School of Mathematics and System Science, Xinjiang University , Urumqi, China"},{"name":"School of Computer Science and Technology, Anhui University , Hefei, China"}]}],"member":"286","published-online":{"date-parts":[[2022,8,21]]},"reference":[{"key":"2022092013215863700_ref1","doi-asserted-by":"crossref","first-page":"101607","DOI":"10.1016\/j.pneurobio.2019.02.003","article-title":"Recent advances in neuropeptide signaling in Drosophila, from genes to physiology and 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