{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,6]],"date-time":"2026-03-06T16:09:27Z","timestamp":1772813367943,"version":"3.50.1"},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"value":"9781643686547","type":"electronic"}],"license":[{"start":{"date-parts":[[2026,3,4]],"date-time":"2026-03-04T00:00:00Z","timestamp":1772582400000},"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":[[2026,3,4]]},"abstract":"<jats:p>The mining of microbial stress-resistance genes is crucial for understanding microbial adaptation mechanisms and has significant implications in biotechnology, agriculture, and environmental science. Traditional methods often rely on homology-based searches, which may miss novel or highly divergent genes. To address this challenge, we propose a novel intelligent computing framework that synergistically integrates signal processing techniques with adaptive deep learning for the de novo identification of stress-resistance genes directly from genomic sequences. Our method begins with a sophisticated preprocessing stage where raw DNA sequences are converted into numerical signals and subsequently decomposed into intrinsic mode functions (IMFs) using Variational Mode Decomposition (VMD). This decomposition effectively disentangles complex sequence patterns and hierarchical features that are often obscured in the raw data. These IMFs are then fed into a Bidirectional Long Short-Term Memory (Bi-LSTM) network, which is adept at capturing long-range contextual dependencies in sequential data. To further enhance the model\u2019s performance and generalization capability, we employ an Artificial Bee Colony (ABC) algorithm to automatically and adaptively optimize the hyperparameters of the Bi-LSTM network, such as the number of hidden layers, learning rate, and dropout rate. Experimental results on a curated dataset of microbial genomes demonstrate that our VMD-ABC-BiLSTM model achieves superior predictive accuracy, precision, and recall compared to standard LSTM models and other machine learning baselines.<\/jats:p>","DOI":"10.3233\/faia260024","type":"book-chapter","created":{"date-parts":[[2026,3,6]],"date-time":"2026-03-06T10:21:05Z","timestamp":1772792465000},"source":"Crossref","is-referenced-by-count":0,"title":["An Intelligent Computing Method for Microbial Stress-Resistant Gene Mining from Sequence Decomposition to Adaptive Deep Learning"],"prefix":"10.3233","author":[{"given":"Ying","family":"Jiang","sequence":"first","affiliation":[{"name":"School of Bioengineering, Qilu University of Technology, Shandong Academy of Sciences, 250353, Jinan, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"7437","container-title":["Frontiers in Artificial Intelligence and Applications","Machine Learning and Artificial Intelligence"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/FAIA260024","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,6]],"date-time":"2026-03-06T10:21:05Z","timestamp":1772792465000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/FAIA260024"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3,4]]},"ISBN":["9781643686547"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/faia260024","relation":{},"ISSN":["0922-6389","1879-8314"],"issn-type":[{"value":"0922-6389","type":"print"},{"value":"1879-8314","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,3,4]]}}}