药物基因组学
机器学习
计算机科学
灵敏度(控制系统)
人工智能
一般化
集合(抽象数据类型)
任务(项目管理)
训练集
计算生物学
药物发现
药物开发
药品
精密医学
数据挖掘
药物反应
预测建模
生物信息学
医学
药理学
生物
病理
数学分析
工程类
经济
管理
程序设计语言
数学
电子工程
作者
Hossein Sharifi-Noghabi,Soheil Jahangiri,Петр Смирнов,C. Suk-Yee Hon,Anthony Mammoliti,Sisira Kadambat Nair,Arvind Singh Mer,Martin Ester,Benjamin Haibe‐Kains
摘要
Abstract The goal of precision oncology is to tailor treatment for patients individually using the genomic profile of their tumors. Pharmacogenomics datasets such as cancer cell lines are among the most valuable resources for drug sensitivity prediction, a crucial task of precision oncology. Machine learning methods have been employed to predict drug sensitivity based on the multiple omics data available for large panels of cancer cell lines. However, there are no comprehensive guidelines on how to properly train and validate such machine learning models for drug sensitivity prediction. In this paper, we introduce a set of guidelines for different aspects of training gene expression-based predictors using cell line datasets. These guidelines provide extensive analysis of the generalization of drug sensitivity predictors and challenge many current practices in the community including the choice of training dataset and measure of drug sensitivity. The application of these guidelines in future studies will enable the development of more robust preclinical biomarkers.
科研通智能强力驱动
Strongly Powered by AbleSci AI