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These miRNAs are typically approximately 20 to 25 nucleotides long. The maturation of miRNAs requires Dicer cleavage at specific sites within the precursor miRNAs (pre-miRNAs). Recent advances in machine learning-based approaches for cleavage site prediction, such as PHDcleav and LBSizeCleav, have been reported. ReCGBM, a gradient boosting-based model, demonstrates superior performance compared with existing methods. Nonetheless, ReCGBM operates solely as a binary classifier despite the presence of two cleavage sites in a typical pre-miRNA. Previous approaches have focused on utilizing only a fraction of the structural information in pre-miRNAs, often overlooking comprehensive secondary structure information. There is a compelling need for the development of a novel model to address these limitations.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Results<\/jats:title>\n                <jats:p>In this study, we developed a deep learning model for predicting the presence of a Dicer cleavage site within a pre-miRNA segment. This model was enhanced by an autoencoder that learned the secondary structure embeddings of pre-miRNA. Benchmarking experiments demonstrated that the performance of our model was comparable to that of ReCGBM in the binary classification tasks. In addition, our model excelled in multi-class classification tasks, making it a more versatile and practical solution than ReCGBM.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Conclusions<\/jats:title>\n                <jats:p>Our proposed model exhibited superior performance compared with the current state-of-the-art model, underscoring the effectiveness of a deep learning approach in predicting Dicer cleavage sites. Furthermore, our model could be trained using only sequence and secondary structure information. Its capacity to accommodate multi-class classification tasks has enhanced the practical utility of our model.<\/jats:p>\n              <\/jats:sec>","DOI":"10.1186\/s12859-024-05638-4","type":"journal-article","created":{"date-parts":[[2024,1,9]],"date-time":"2024-01-09T10:04:05Z","timestamp":1704794645000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["DiCleave: a deep learning model for predicting human Dicer cleavage sites"],"prefix":"10.1186","volume":"25","author":[{"given":"Lixuan","family":"Mu","sequence":"first","affiliation":[]},{"given":"Jiangning","family":"Song","sequence":"additional","affiliation":[]},{"given":"Tatsuya","family":"Akutsu","sequence":"additional","affiliation":[]},{"given":"Tomoya","family":"Mori","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,1,9]]},"reference":[{"key":"5638_CR1","doi-asserted-by":"publisher","first-page":"402","DOI":"10.3389\/fendo.2018.00402","volume":"9","author":"J O'Brien","year":"2018","unstructured":"O\u2019Brien J, Hayder H, Zayed Y, Peng C. 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