准确医学时代的药物反应:一个方法论审查
Daniella Okyere1, Laura Bravo-Merodio1,2,3, Yuanwei Xu1,2,3
1Cancer and Genomic Sciences, University of Birmingham, Birmingham, UK.
Computational and structural biotechnology journal
|December 25, 2025
概括
本综述通过基于数据-响应关系的计算方法进行分类来统一药物反应预测方法. 它强调了通过数据驱动的药物发现来推进精准医学的关键挑战和未来方向.
科学领域:
- 计算生物学是一种计算生物学.
- 生物信息学是一种生物信息学.
- 药物基因组学 药物基因组学
背景情况:
- 结构化 (分子,药理) 和非结构化 (医学成像) 数据越来越多.
- 像机器学习 (ML) 和深度学习 (DL) 这样的计算方法对于准确医学中药物反应预测至关重要.
研究的目的:
- 系统地审查现有的药物反应预测方法.
- 基于数据-响应关系提出统一的分类框架.
- 在各种疾病和数据类型中比较基于统计和ML的模型.
主要方法:
- 系统审查用于药物反应预测的计算方法.
- 基于数据-响应关系的分类框架:单个数据类型与响应向量/矩阵,多个数据类型与响应.
- 分析输入数据结构,响应变量定义和使用的数据类型.
主要成果:
- 一个统一的框架来分类基于数据-响应关系的药物反应预测模型.
- 在不同疾病和数据类型中比较统计和ML模型.
- 确定该领域的新兴趋势和关键挑战.
结论:
- 拟议的框架有助于全面比较用于药物反应预测的各种计算模型.
- 数据整合和方法创新的进步对于准确医学的未来进步至关重要.
- 应对关键挑战将加速开发更准确和个性化的治疗策略.
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