MSDRP:基于多源数据的深度学习模型,用于预测药物反应
Haochen Zhao1,2, Xiaoyu Zhang1,2, Qichang Zhao1,2
1Hunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha 410083, China.
Bioinformatics (Oxford, England)
|August 22, 2023
概括
预测癌症药物反应对于个性化治疗至关重要. 一个新的深度学习模型,MSDRP,集成了药物-生物实体相互作用和药物-细胞系相互作用,优于改善治疗策略的现有方法.
科学领域:
- 计算生物学是一种计算生物学.
- 药物基因组学 药物基因组学
- 机器学习在瘤学中
背景情况:
- 癌症异质性复杂化治疗结果,需要准确的体外药物反应预测个性化医学.
- 现有的计算模型往往忽略了药物和生物实体 (目标,疾病,副作用) 之间的关键关系,以及双对药物细胞系相互作用.
研究的目的:
- 开发一种新的深度学习框架,MSDRP,用于预测体外药物反应.
- 通过整合多源药物-生物实体关联和药物-细胞系相互作用来提高药物反应预测.
主要方法:
- 拟议的MSDRP是一个深度学习框架,包含药物细胞系关系的交互模块.
- 利用相似性网络融合算法来整合多种药物生物实体的关联.
- 从多源药物相似性矩阵中获得的使用特征向量.
主要成果:
- 在实验中,MSDRP在所有性能指标上都超过了最先进的模型.
- 新的和独立的测试表明MSDRP在预测对新药的反应方面表现出色.
- 案例研究证实了该模型的可解释性和多源药物相似性特征的有用性.
结论:
- 通过捕捉复杂的相互作用,MSDRP框架有效预测体外药物反应.
- 整合多种来源的药物-生物实体数据和药物-细胞系相互作用显著提高了预测的准确性.
- 通过准确的药物反应预测,MSDRP为推进个性化癌症治疗提供了一个有前途的工具.
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