{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,19]],"date-time":"2026-02-19T02:10:11Z","timestamp":1771467011739,"version":"3.50.1"},"reference-count":51,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2023,2,16]],"date-time":"2023-02-16T00:00:00Z","timestamp":1676505600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>MicroRNAs (miRNA) are small, non-coding regulatory molecules whose effective alteration might result in abnormal gene manifestation in the downstream pathway of their target. miRNA gene variants can impact miRNA transcription, maturation, or target selectivity, impairing their usefulness in plant growth and stress responses. Simple Sequence Repeat (SSR) based on miRNA is a newly introduced functional marker that has recently been used in plant breeding. MicroRNA and long non-coding RNA (lncRNA) are two examples of non-coding RNA (ncRNA) that play a vital role in controlling the biological processes of animals and plants. According to recent studies, the major objective for decoding their functional activities is predicting the relationship between lncRNA and miRNA. Traditional feature-based classification systems\u2019 prediction accuracy and reliability are frequently harmed because of the small data size, human factors\u2019 limits, and huge quantity of noise. This paper proposes an optimized deep learning model built with Independently Recurrent Neural Networks (IndRNNs) and Convolutional Neural Networks (CNNs) to predict the interaction in plants between lncRNA and miRNA. The deep learning ensemble model automatically investigates the function characteristics of genetic sequences. The proposed model\u2019s main advantage is the enhanced accuracy in plant miRNA\u2013IncRNA prediction due to optimal hyperparameter tuning, which is performed by the artificial Gorilla Troops Algorithm and the proposed intelligent preying algorithm. IndRNN is adapted to derive the representation of learned sequence dependencies and sequence features by overcoming the inaccuracies of natural factors in traditional feature architecture. Working with large-scale data, the suggested model outperforms the current deep learning model and shallow machine learning, notably for extended sequences, according to the findings of the experiments, where we obtained an accuracy of 97.7% in the proposed method.<\/jats:p>","DOI":"10.3390\/s23042219","type":"journal-article","created":{"date-parts":[[2023,2,17]],"date-time":"2023-02-17T01:32:56Z","timestamp":1676597576000},"page":"2219","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["An Optimized Ensemble Deep Learning Model for Predicting Plant miRNA\u2013IncRNA Based on Artificial Gorilla Troops Algorithm"],"prefix":"10.3390","volume":"23","author":[{"given":"Walid","family":"Hamdy","sequence":"first","affiliation":[{"name":"Faculty of Science, Port Said University, Port Said 42511, Egypt"}]},{"given":"Amr","family":"Ismail","sequence":"additional","affiliation":[{"name":"Faculty of Science, Port Said University, Port Said 42511, Egypt"}]},{"given":"Wael A.","family":"Awad","sequence":"additional","affiliation":[{"name":"Faculty of Computers and Artificial Intelligence, Damietta University, El-Gadeeda 34519, Egypt"}]},{"given":"Ali H.","family":"Ibrahim","sequence":"additional","affiliation":[{"name":"Faculty of Science, Port Said University, Port Said 42511, Egypt"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9989-6681","authenticated-orcid":false,"given":"Aboul Ella","family":"Hassanien","sequence":"additional","affiliation":[{"name":"Faculty of Computers and Artificial Intelligence, Cairo University, Giza 12613, Egypt"}]}],"member":"1968","published-online":{"date-parts":[[2023,2,16]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"e25146","DOI":"10.1097\/MD.0000000000025146","article-title":"Relationship between long non-coding RNA polymorphism and the risk of coronary artery disease: A protocol for systematic review and meta-analysis","volume":"100","author":"Bolin","year":"2021","journal-title":"Medicine"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Fonouni-Farde, C., Ariel, F., and Crespi, M. 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