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The knowledge about the 4mC sites is a key foundation to exploring its roles. Due to the limitation of techniques, precise detection of 4mC is still a challenging task. In this paper, we presented a multi-scale convolution neural network (CNN) and adaptive embedding-based computational method for predicting 4mC sites in mouse genome, which was referred to as MultiScale-CNN-4mCPred. The MultiScale-CNN-4mCPred used adaptive embedding to encode nucleotides, and then utilized multi-scale CNNs as well as long short-term memory to extract more in-depth local properties and contextual semantics in the sequences. The MultiScale-CNN-4mCPred is an end-to-end learning method, which requires no sophisticated feature design. The MultiScale-CNN-4mCPred reached an accuracy of 81.66% in the 10-fold cross-validation, and an accuracy of 84.69% in the independent test, outperforming state-of-the-art methods. We implemented the proposed method into a user-friendly web application which is freely available at: <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"uri\" xlink:href=\"http:\/\/www.biolscience.cn\/MultiScale-CNN-4mCPred\/\">http:\/\/www.biolscience.cn\/MultiScale-CNN-4mCPred\/<\/jats:ext-link>.<\/jats:p>","DOI":"10.1186\/s12859-023-05135-0","type":"journal-article","created":{"date-parts":[[2023,1,18]],"date-time":"2023-01-18T10:02:57Z","timestamp":1674036177000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["MultiScale-CNN-4mCPred: a multi-scale CNN and adaptive embedding-based method for mouse genome DNA N4-methylcytosine prediction"],"prefix":"10.1186","volume":"24","author":[{"given":"Peijie","family":"Zheng","sequence":"first","affiliation":[]},{"given":"Guiyang","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Yuewu","family":"Liu","sequence":"additional","affiliation":[]},{"given":"Guohua","family":"Huang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,1,18]]},"reference":[{"key":"5135_CR1","doi-asserted-by":"publisher","first-page":"590","DOI":"10.1038\/s41580-019-0159-6","volume":"20","author":"MVC Greenberg","year":"2019","unstructured":"Greenberg MVC, Bourc\u2019his D. 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