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针对抗癌药物组合的机器学习模型:分析,预测和验证

Jing-Bo Zhou1, Dongyang Tang1, Lin He1

  • 1Cancer Center, Faculty of Health Sciences, University of Macau, Macau SAR, China; Centre for Precision Medicine Research and Training, Faculty of Health Sciences, University of Macau, Macau SAR, China.

Pharmacological research
|June 21, 2023
PubMed
概括

确定用于癌症治疗的协同药物组合是具有挑战性的. 这项研究开发了机器学习模型来预测有效的药物对,在实验室实验中验证了它们,并确定了Lapatinib和Pazopanib作为乳腺癌的有希望的组合.

关键词:
博尔特佐米布 (PubChem CID: 387447) 是一种药物.西法兰丁 (PubChem CID: 10206) 是一种多塞塔克塞尔 (Taxotere) (PubChem CID: 11970251) 的使用情况药物组合治疗是药物组合治疗.弗卢纳瑞二化物 (PubChem CID: 5282407) 是一种拉帕提尼布二酸盐 (PubChem CID: 9941095) 的使用情况机器学习是机器学习.帕佐帕尼布化 (PubChem CID: 11525740) 是一种潜在的组合生物标志物塔莫西芬酸盐 (PubChem CID: 2733525) 是一种三维瘤切片培养方法瓦塔兰尼布 (PubChem CID: 151194) 是一种药物.文克里斯 (PubChem CID: 5978) 的使用情况XL-184 (卡博赞提尼布) (PubChem CID: 25102847) 的使用情况

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