Nam Le

Combinatorial Drug Recommendation

Nam Le
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Combinatorial Drug Recommendation #

Combinatorial Drug Recommendation involves finding optimal combinations of drugs to maximize therapeutic effects while minimizing adverse interactions, a key application in personalized medicine and drug discovery.

Recent Literature #

  1. Learning Combinatorial Drug Recommendations via Graph Neural Networks Nature Medicine, 2023. paper

    Xin He, Yong Liu, Ying Wei, Yuqiao Zhang, Yizhou Wang

  2. Graph Neural Networks for Drug-Drug Interactions Bioinformatics, 2021. paper, code

    Yu-Hao Yang, Fan Chen, Yajun Wang, Kun Huang

  3. Deep Learning Approaches for Drug Combination Analysis Nature Computational Science, 2022. paper

    Jing Yang, Fang Liu, Yung-Jen Chen, Kimberly Glass, Jill P. Mesirov

  4. Knowledge-Guided Neural Networks for Drug Interaction Prediction Briefings in Bioinformatics, 2023. paper

    Xiaowan Kuang, Yihang Pan, Hongmin Cai, Wentao Liu, De-Shuang Huang

  5. Synergistic Drug Interaction Prediction NeurIPS 2023 Workshop on AI for Drug Discovery, Biodesign and Therapeutics, 2023. paper

    Chen Wen, Xiaowei Zhang, Tengfei Ma

  6. Explainable Machine Learning for Drug Combinations Machine Learning for Healthcare, 2023. paper

    Nathan Leung, Jingxi Jessica Lu, Michael Vigh

  7. Transfer Learning for Combinatorial Drug Sensitivity Prediction IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2023. paper

    Zheng Zhang, Jing Ma, Yong Liu

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