癌症组合治疗预测中的计算进步
Victoria L Flanary1, Jennifer L Fisher1, Elizabeth J Wilk1
1Department of Cell, Developmental and Integrative Biology, Heersink School of Medicine, The University of Alabama at Birmingham, Birmingham, AL.
JCO precision oncology
|October 12, 2023
概括
计算药物重新定位通过预测有效的药物组合来帮助癌症治疗. 这篇评论探讨了机器学习和深度学习等方法,以确定新的癌症治疗方法.
科学领域:
- 计算生物学是一种计算生物学.
- 药理学 药理学是指药理学的学科.
- 在瘤学瘤学.
背景情况:
- 在新的药物发现中,高消耗率需要具有成本效益的替代品.
- 单剂癌症疗法往往表现出有限的疗效.
- 在形中,药物重新利用为识别新型组合疗法提供了一个有希望的策略.
研究的目的:
- 引入用于预测癌症组合疗法的计算方法.
- 要总结最近使用这些预测方法的研究.
- 突出改善未来预测模型的考虑因素.
主要方法:
- 在PubMed的系统文献搜索 (过去10年).
- 包括关于正在进行的/回顾性研究的评论和文章.
- 专注于改善组合治疗预测的方法.
主要成果:
- 计算方法包括网络分析,基于回归的机器学习,分类器机器学习和深度学习.
- 每个方法类都有独特的优点和缺点.
- 优先考虑提供方法改进见解的研究.
结论:
- 仔细选择计算方法对于设计有效的预测策略至关重要.
- 未来的改进包括整合疾病病理生物学,药物特征,多组数据和药物相互作用.
- 多种数据的先进计算集成将使得安全有效的癌症组合疗法的更准确的预测成为可能.
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