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However, how to integrate and analyze these single-cell multi-omics data remains a great challenge. Herein, we introduce an interpretable multitask framework (scMoMtF) for comprehensively analyzing single-cell multi-omics data. The scMoMtF can simultaneously solve multiple key tasks of single-cell multi-omics data including dimension reduction, cell classification and data simulation. The experimental results shows that scMoMtF outperforms current state-of-the-art algorithms on these tasks. In addition, scMoMtF has interpretability which allowing researchers to gain a reliable understanding of potential biological features and mechanisms in single-cell multi-omics data.<\/jats:p>","DOI":"10.1371\/journal.pcbi.1012679","type":"journal-article","created":{"date-parts":[[2024,12,18]],"date-time":"2024-12-18T18:28:12Z","timestamp":1734546492000},"page":"e1012679","update-policy":"https:\/\/doi.org\/10.1371\/journal.pcbi.corrections_policy","source":"Crossref","is-referenced-by-count":21,"title":["scMoMtF: An interpretable multitask learning framework for single-cell multi-omics data 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