Causal inference in drug discovery and development

被引:7
|
作者
Michoel, Tom [1 ]
Zhang, Jitao David [2 ,3 ]
机构
[1] Univ Bergen, Dept Informat, Computat Biol Unit, POB 7803, N-5020 Bergen, Norway
[2] F Hoffmann La Roche, Roche Innovat Ctr Basel, Pharm Early Res & Dev, Grenzacherstr 124, CH-4070 Basel, Switzerland
[3] Univ Basel, Dept Math & Comp Sci, Spiegelgasse 1, CH-4051 Basel, Switzerland
关键词
causality; causal inference; DAG (standing for directed acyclic graph); drug discovery; drug development; reverse translation; INTEGRATIVE GENOMICS APPROACH; MENDELIAN RANDOMIZATION; GENE NETWORKS; SYSTEMS GENETICS; MODELS; RECONSTRUCTION; IDENTIFICATION; ENVIRONMENT; ASSOCIATION; EXPRESSION;
D O I
10.1016/j.drudis.2023.103737
中图分类号
R9 [药学];
学科分类号
1007 ;
摘要
To discover new drugs is to seek and to prove causality. As an emerging approach leveraging human knowledge and creativity, data, and machine intelligence, causal inference holds the promise of reducing cognitive bias and improving decision-making in drug discovery. Although it has been applied across the value chain, the concepts and practice of causal inference remain obscure to many practitioners. This article offers a nontechnical introduction to causal inference, reviews its recent applications, and discusses opportunities and challenges of adopting the causal language in drug discovery and development.
引用
收藏
页数:17
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