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Selecting the most potential circRNA-related miRNAs and taking advantage of them as the biological markers or drug targets could be conducive to dealing with complex human diseases through preventive strategies, diagnostic procedures and therapeutic approaches. Compared to traditional biological experiments, leveraging computational models to integrate diverse biological data in order to infer potential associations proves to be a more efficient and cost-effective approach. This paper developed a model of Convolutional Autoencoder for CircRNA\u2013MiRNA Associations (CA-CMA) prediction. Initially, this model merged the natural language characteristics of the circRNA and miRNA sequence with the features of circRNA\u2013miRNA interactions. Subsequently, it utilized all circRNA\u2013miRNA pairs to construct a molecular association network, which was then fine-tuned by labeled samples to optimize the network parameters. Finally, the prediction outcome is obtained by utilizing the deep neural networks classifier. This model innovatively combines the likelihood objective that preserves the neighborhood through optimization, to learn the continuous feature representation of words and preserve the spatial information of two-dimensional signals. During the process of 5-fold cross-validation, CA-CMA exhibited exceptional performance compared to numerous prior computational approaches, as evidenced by its mean area under the receiver operating characteristic curve of 0.9138 and a minimal SD of 0.0024. Furthermore, recent literature has confirmed the accuracy of 25 out of the top 30 circRNA\u2013miRNA pairs identified with the highest CA-CMA scores during case studies. The results of these experiments highlight the robustness and versatility of our model.<\/jats:p>","DOI":"10.1093\/bib\/bbae020","type":"journal-article","created":{"date-parts":[[2024,2,7]],"date-time":"2024-02-07T17:53:25Z","timestamp":1707328405000},"source":"Crossref","is-referenced-by-count":34,"title":["Likelihood-based feature representation learning combined with neighborhood information for predicting circRNA\u2013miRNA associations"],"prefix":"10.1093","volume":"25","author":[{"given":"Lu-Xiang","family":"Guo","sequence":"first","affiliation":[{"name":"School of Computer Science and Technology, China University of Mining and Technology , Xuzhou, 221116 , China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0184-307X","authenticated-orcid":false,"given":"Lei","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, China University of Mining and Technology , Xuzhou, 221116 , 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China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8200-6016","authenticated-orcid":false,"given":"Bo-Wei","family":"Zhao","sequence":"additional","affiliation":[{"name":"Xinjiang Technical Institute of Physics and Chemistry , Chinese Academy of Sciences, Urumqi 830011 , China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yang","family":"Li","sequence":"additional","affiliation":[{"name":"School of Computer Science and Information Engineering, Hefei University of Technology , Hefei 230601 , China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2024,2,7]]},"reference":[{"key":"2024031210410497300_ref1","doi-asserted-by":"crossref","first-page":"3659","DOI":"10.1002\/adfm.201100963","article-title":"Encapsulation of RNA molecules in BSA microspheres and internalization into Trypanosoma brucei parasites and human U2OS cancer 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