解释乳腺癌中的药物协同作用与深度学习,使用目标蛋白抑制配置文件
Thanyawee Srithanyarat1,2, Kittisak Taoma1,2, Thana Sutthibutpong3,4
1Bioinformatics and Systems Biology Program, School of Bioresources and Technology, King Mongkut's University of Technology Thonburi, Bangkok, 10150, Thailand.
BioData mining
|February 29, 2024
概括
这项研究引入了一个可解释的深度学习模型,通过分析蛋白质抑制来预测乳腺癌治疗中的药物协同作用. 该模型提供了生物学见解,有助于开发更有效的组合疗法.
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:
- 乳腺癌是全球女性的主要恶性瘤.
- 耐药性和副作用限制了当前的乳腺癌治疗方法.
- 药物组合越来越多地被探索以提高治疗效率.
研究的目的:
- 开发一个可解释的深度学习模型来预测药物协同作用.
- 利用蛋白抑制数据作为协同预测的特征.
- 克服现有的深度学习模型的解释性挑战.
主要方法:
- 开发了一个深度神经网络,以预测小分子药物对之间的协同作用.
- 该模型使用对13种关键蛋白质的抑制活动作为输入特征.
- 在五个乳腺癌细胞系中评估了模型的性能.
主要成果:
- 该模型在预测和实验协同作用之间实现了0.63的皮尔森相关性.
- 在BT-549和MCF-7细胞系中观察到最高的相关性 (0.67).
- 该模型的可解释性允许洞察通过 protein 的协同相互作用模式.
结论:
- 开发的框架提高了药物协同效应预测中的模型解释性.
- 将深度学习与特定目标数据相结合,为乳腺癌治疗提供了一个有前途的方法.
- 识别目标蛋白抑制特征可以指导开发新型组合疗法.
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