{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,11]],"date-time":"2026-05-11T22:55:33Z","timestamp":1778540133728,"version":"3.51.4"},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"value":"9781643686318","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,10,21]],"date-time":"2025-10-21T00:00:00Z","timestamp":1761004800000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,10,21]]},"abstract":"<jats:p>N4-acetylcytidine (ac4C) is a crucial RNA modification associated with mRNA stability and translational efficiency. Accurate identification of ac4C sites is essential for understanding their regulatory functions. However, experimental detection remains expensive and labor-intensive. At the same time, existing computational models suffer from limited generalization and insufficient feature discrimination, especially in distinguishing subtle nucleotide patterns. In this work, we propose a deep learning model named SNN-ac4C, which is based on a contrastive learning-based neural network. The model integrates a dual-path structure that combines BiLSTM and Multi-Head Self-Attention (MHSA) for capturing long-range dependencies and global context, while using CNN to extract local biological sequence features. The contrastive learning module further enhances the discriminative ability of ac4C and Non-ac4C sites by increasing the separation between positive and negative samples. Experiments on the test set confirm the effectiveness of SNN-ac4C, which achieves an accuracy (ACC) of 84.60% and a Matthews Correlation Coefficient (MCC) of 0.6934. Compared with NBCR-ac4C, the current state-of-the-art model, SNN-ac4C improves ACC and MCC by 1.09% and 0.0228, respectively. The source code and relevant supplementary are publicly available at https:\/\/github.com\/2103374200\/SNN.<\/jats:p>","DOI":"10.3233\/faia251061","type":"book-chapter","created":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T09:50:18Z","timestamp":1761126618000},"source":"Crossref","is-referenced-by-count":2,"title":["Catching mRNA\u2019s Hiddens Marks: A Dual-Path Network by Contrastive Learning for N4-acetylcytidine Prediction"],"prefix":"10.3233","author":[{"given":"Wenying","family":"He","sequence":"first","affiliation":[{"name":"School of Artificial Intelligence, Hebei University of Technology, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yu","family":"Han","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence, Hebei University of Technology, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Haolu","family":"Zhou","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence, Hebei University of Technology, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yun","family":"Zuo","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence and Computer Science, Jiangnan University, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yude","family":"Bai","sequence":"additional","affiliation":[{"name":"School of Software, Tiangong University, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fei","family":"Guo","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Central South University, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"7437","container-title":["Frontiers in Artificial Intelligence and Applications","ECAI 2025"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/FAIA251061","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T09:50:18Z","timestamp":1761126618000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/FAIA251061"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,21]]},"ISBN":["9781643686318"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/faia251061","relation":{},"ISSN":["0922-6389","1879-8314"],"issn-type":[{"value":"0922-6389","type":"print"},{"value":"1879-8314","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,10,21]]}}}