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Michoel T, Zhang JD. Causal inference in drug discovery and development. Drug Discov Today 2023; 28:103737. [PMID: 37591410 DOI: 10.1016/j.drudis.2023.103737] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2022] [Revised: 07/31/2023] [Accepted: 08/10/2023] [Indexed: 08/19/2023]
Abstract
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.
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Affiliation(s)
- Tom Michoel
- Computational Biology Unit, Department of Informatics, University of Bergen, Postboks 7803, 5020 Bergen, Norway
| | - Jitao David Zhang
- Pharma Early Research and Development, Roche Innovation Centre Basel, F. Hoffmann-La Roche, Grenzacherstrasse 124, 4070 Basel, Switzerland; Department of Mathematics and Computer Science, University of Basel, Spiegelgasse 1, 4051 Basel, Switzerland.
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Pomiès L, Brouard C, Duruflé H, Maigné É, Carré C, Gody L, Trösser F, Katsirelos G, Mangin B, Langlade NB, de Givry S. Gene regulatory network inference methodology for genomic and transcriptomic data acquired in genetically related heterozygote individuals. Bioinformatics 2022; 38:4127-4134. [PMID: 35792837 DOI: 10.1093/bioinformatics/btac445] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2021] [Revised: 06/17/2022] [Accepted: 07/05/2022] [Indexed: 12/24/2022] Open
Abstract
MOTIVATION Inferring gene regulatory networks in non-independent genetically related panels is a methodological challenge. This hampers evolutionary and biological studies using heterozygote individuals such as in wild sunflower populations or cultivated hybrids. RESULTS First, we simulated 100 datasets of gene expressions and polymorphisms, displaying the same gene expression distributions, heterozygosities and heritabilities as in our dataset including 173 genes and 353 genotypes measured in sunflower hybrids. Secondly, we performed a meta-analysis based on six inference methods [least absolute shrinkage and selection operator (Lasso), Random Forests, Bayesian Networks, Markov Random Fields, Ordinary Least Square and fast inference of networks from directed regulation (Findr)] and selected the minimal density networks for better accuracy with 64 edges connecting 79 genes and 0.35 area under precision and recall (AUPR) score on average. We identified that triangles and mutual edges are prone to errors in the inferred networks. Applied on classical datasets without heterozygotes, our strategy produced a 0.65 AUPR score for one dataset of the DREAM5 Systems Genetics Challenge. Finally, we applied our method to an experimental dataset from sunflower hybrids. We successfully inferred a network composed of 105 genes connected by 106 putative regulations with a major connected component. AVAILABILITY AND IMPLEMENTATION Our inference methodology dedicated to genomic and transcriptomic data is available at https://forgemia.inra.fr/sunrise/inference_methods. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Lise Pomiès
- MIAT, Université Fédérale de Toulouse, INRAE, Castanet-Tolosan 31326, France
| | - Céline Brouard
- MIAT, Université Fédérale de Toulouse, INRAE, Castanet-Tolosan 31326, France
| | - Harold Duruflé
- LIPME, Université de Toulouse, INRAE, CNRS, Castanet-Tolosan 31326, France
| | - Élise Maigné
- MIAT, Université Fédérale de Toulouse, INRAE, Castanet-Tolosan 31326, France
| | - Clément Carré
- MIAT, Université Fédérale de Toulouse, INRAE, Castanet-Tolosan 31326, France
| | - Louise Gody
- LIPME, Université de Toulouse, INRAE, CNRS, Castanet-Tolosan 31326, France
| | - Fulya Trösser
- MIAT, Université Fédérale de Toulouse, INRAE, Castanet-Tolosan 31326, France
| | - George Katsirelos
- MIA-Paris, AgroParisTech, Université Paris-Saclay, INRAE, Paris 75231, France
| | - Brigitte Mangin
- LIPME, Université de Toulouse, INRAE, CNRS, Castanet-Tolosan 31326, France
| | - Nicolas B Langlade
- LIPME, Université de Toulouse, INRAE, CNRS, Castanet-Tolosan 31326, France
| | - Simon de Givry
- MIAT, Université Fédérale de Toulouse, INRAE, Castanet-Tolosan 31326, France
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