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In this study, we present a deep learning-based framework named Deep neural network Integrating Prior Knowledge (DIPK) (DIPK), which adopts self-supervised techniques to integrate multiple valuable information, including gene interaction relationships, gene expression profiles and molecular topologies, to enhance prediction accuracy and robustness. We demonstrated the superior performance of DIPK compared to existing methods on both known and novel cells and drugs, underscoring the importance of gene interaction relationships in drug response prediction. In addition, DIPK extends its applicability to single-cell RNA sequencing data, showcasing its capability for single-cell-level response prediction and cell identification. Further, we assess the applicability of DIPK on clinical data. DIPK accurately predicted a higher response to paclitaxel in the pathological complete response (pCR) group compared to the residual disease group, affirming the better response of the pCR group to the chemotherapy compound. We believe that the integration of DIPK into clinical decision-making processes has the potential to enhance individualized treatment strategies for cancer patients.<\/jats:p>","DOI":"10.1093\/bib\/bbae153","type":"journal-article","created":{"date-parts":[[2024,4,11]],"date-time":"2024-04-11T03:28:01Z","timestamp":1712806081000},"source":"Crossref","is-referenced-by-count":28,"title":["Improving drug response prediction via integrating gene relationships with deep learning"],"prefix":"10.1093","volume":"25","author":[{"given":"Pengyong","family":"Li","sequence":"first","affiliation":[{"name":"School of Computer Science and Technology , , 710126 Xi\u2019an, Shaanxi , China"},{"name":"Xidian University , , 710126 Xi\u2019an, Shaanxi , China"},{"name":"State Key Laboratory of Quality Research in Chinese Medicine , Macau Institute for Applied Research in Medicine and Health, , 519020 Macau , China"},{"name":"Macau University of Science and Technology , Macau Institute for Applied Research in Medicine and Health, , 519020 Macau , China"}]},{"given":"Zhengxiang","family":"Jiang","sequence":"additional","affiliation":[{"name":"School of Electronic Engineering, Xidian University , 710126 Xi\u2019an, Shaanxi , China"}]},{"given":"Tianxiao","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology , , 710126 Xi\u2019an, Shaanxi , China"},{"name":"Xidian University , , 710126 Xi\u2019an, Shaanxi , China"}]},{"given":"Xinyu","family":"Liu","sequence":"additional","affiliation":[{"name":"Beijing Laboratory of Biomedical Materials , Department of Geriatric Dentistry, , 100081 Beijing , China"},{"name":"Peking University School and Hospital of Stomatology , Department of Geriatric Dentistry, , 100081 Beijing , China"}]},{"given":"Hui","family":"Qiao","sequence":"additional","affiliation":[{"name":"Department of Oncology, Tai\u2019an Municipal Hospital , 271021 Tai\u2019an, Shandong , 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