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Accurately predicting miRNA targets is essential for developing miRNA-based therapies for diseases such as cancer and cardiovascular disease. Traditional miRNA target prediction methods often struggle due to incomplete knowledge of miRNA-target interactions and lack interpretability. To address these limitations, we propose miCGR, an end-to-end deep learning framework for predicting functional miRNA targets. MiCGR employs 2D convolutional neural networks alongside an enhanced Chaos Game Representation (CGR) of both miRNA sequences and their candidate target site (CTS) on mRNA. This advanced CGR transforms genetic sequences into informative 2D graphical representations based on sequence composition and subsequence frequencies, and explicitly incorporates important prior knowledge of seed regions and subsequence positions. Unlike one-dimensional methods based solely on sequence characters, this approach identifies functional motifs within sequences, even if they are distant in the original sequences. Our model outperforms existing methods in predicting functional targets at both the site and gene levels. To enhance interpretability, we incorporate Shapley value analysis for each subsequence within both miRNA sequences and their target sites, allowing miCGR to achieve improved accuracy, particularly with more lenient CTS selection criteria. Finally, two case studies demonstrate the practical applicability of miCGR, highlighting its potential to provide insights for optimizing artificial miRNA analogs that surpass endogenous counterparts.<\/jats:p>","DOI":"10.1093\/bib\/bbae616","type":"journal-article","created":{"date-parts":[[2024,11,27]],"date-time":"2024-11-27T00:45:36Z","timestamp":1732668336000},"source":"Crossref","is-referenced-by-count":4,"title":["miCGR: interpretable deep neural network for predicting both site-level and gene-level functional targets of microRNA"],"prefix":"10.1093","volume":"26","author":[{"given":"Xiaolong","family":"Wu","sequence":"first","affiliation":[{"name":"School of Pharmacy, East China University of Science and Technology , 130 Meilong Road, Shanghai 200237 ,","place":["China"]},{"name":"Drug Discovery and Design Center , State Key Laboratory of Drug Research, , 555 Zuchongzhi Road, Shanghai 201203 ,","place":["China"]},{"name":"Shanghai Institute of Materia Medica Chinese Academy of 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Design Center , State Key Laboratory of Drug Research, , 555 Zuchongzhi Road, Shanghai 201203 ,","place":["China"]},{"name":"Shanghai Institute of Materia Medica Chinese Academy of Sciences , State Key Laboratory of Drug Research, , 555 Zuchongzhi Road, Shanghai 201203 ,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiangtai","family":"Kong","sequence":"additional","affiliation":[{"name":"Drug Discovery and Design Center , State Key Laboratory of Drug Research, , 555 Zuchongzhi Road, Shanghai 201203 ,","place":["China"]},{"name":"Shanghai Institute of Materia Medica Chinese Academy of Sciences , State Key Laboratory of Drug Research, , 555 Zuchongzhi Road, Shanghai 201203 ,","place":["China"]},{"name":"School of Pharmacy, University of Chinese Academy of Sciences , No. 19A Yuquan Road, Beijing 100049 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