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Given the inherent challenges in experimental processes, computational methods for in silico peptide identification can be invaluable for enabling high\u2010throughput characterization of ACE inhibitory peptides. This study introduces GRU4ACE, an innovative deep learning framework based on multi\u2010view information for identifying ACE inhibitory peptides. First, GRU4ACE utilizes multi\u2010source feature encoding methods to capture the information embedded in ACE inhibitory peptides, including sequential information, graphical information, semantic information, and contextual information. Specifically, the feature representations used herein are derived from conventional feature descriptors, natural language processing (NLP)\u2010based embeddings, and pre\u2010trained protein language model (PLM)\u2010based embeddings. Next, multiple feature embeddings were fused, and the elastic net was employed for feature optimization. Finally, the optimal feature subset with strong feature representation was input into a gated recurrent unit (GRU). The proposed GRU4ACE approach demonstrated superior performance over existing methods in terms of the independent test. To be specific, the balanced accuracy, sensitivity, and MCC scores of GRU4ACE reached 0.948, 0.934, and 0.895, which were 6.46%, 8.92%, and 12.51% higher than those of the compared methods, respectively. In addition, when comparing well\u2010regarded feature descriptors, we found that the proposed multi\u2010view features effectively captured crucial information, leading to improved ACE inhibitory peptide prediction performance. These comprehensive results highlight that GRU4ACE enhances prediction accuracy and significantly narrows down the search for new potential antihypertensive drugs.<\/jats:p>","DOI":"10.1002\/pro.70026","type":"journal-article","created":{"date-parts":[[2025,5,15]],"date-time":"2025-05-15T06:31:14Z","timestamp":1747290674000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["<scp>GRU4ACE<\/scp>\n                    : Enhancing\n                    <scp>ACE<\/scp>\n                    inhibitory peptide prediction by integrating gated recurrent unit with multi\u2010source feature embeddings"],"prefix":"10.1002","volume":"34","author":[{"given":"Saeed","family":"Ahmed","sequence":"first","affiliation":[{"name":"Center for Research Innovation and Biomedical Informatics, Faculty of Medical Technology Mahidol University  Bangkok Thailand"},{"name":"Department of Computer Science University of Swabi  Swabis 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