Topological Data Analysis and its usefulness for precision medicine studies
Iniesta, Raquel (King's College London. Institute of Psychiatry, Psychology & Neuroscience)
Carr, Ewan (King's College London. Institute of Psychiatry, Psychology & Neuroscience)
Carrière, Mathieu (Inria Sophia-Antipolis, DataShape Team, Biot, France)
Yerolemou, Naya (The University of Oxford and The Alan Turing Institute)
Michel, Bertrand (Ecole Centrale de Nantes, Laboratoire de Mathématiques Jean Leray - Unité mixte de recherche Centre National de la Recherche Scientifique)
Chazal, Frédéric (Inria Saclay - Ile-de-France)

Data:	2022
Resum:	Precision medicine allows the extraction of information from complex datasets to facilitate clinical decision-making at the individual level. Topological Data Analysis (TDA) offers promising tools that complement current analytical methods in precision medicine studies. We introduce the fundamental concepts of the TDA corpus (the simplicial complex, the Mapper graph, the persistence diagram and persistence landscape). We show how these can be used to enhance the prediction of clinical outcomes and to identify novel subpopulations of interest, particularly applied to understand remission of depression in data from the GENDEP clinical trial.
Ajuts:	European Commission FP7/2007-2013
Nota:	Acknowledgements. This work was supported by a 2017 NARSAD Young Investigator Grant from the Brain & Behavior Research Foundation granted to Dr Raquel Iniesta. This work has been funded by the European Commission Framework 6 grant, EC Contract LSHB-CT-2003-503428 and an Innovative Medicine Initiative Joint Undertaking (IMI-JU) grant n-115008 of which resources are composed of European Union and the European Federation of Pharmaceutical Industries and Associations (EFPIA) in-kind contribution and financial contribution from the European Union's Seventh Framework Programme (FP7/2007-2013). This paper represents independent research part funded by the National Institute for Health Research (NIHR) Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King's College London. The authors further acknowledge use of the research computing facility at King's College London, Rosalind (https: //rosalind.kcl.ac.uk), which is delivered in partnership with the National Institute for Health Research (NIHR) Biomedical Research Centres at South London Maudsley and Guy's St. Thomas' NHS Foundation Trusts, and part-funded by capital equipment grants from the Maudsley Charity (award 980) and Guy's St. Thomas' Charity (TR130505). The views expressed are those of the author(s) and not necessarily those of the NHS, the NIHR or the Department of Health and Social Care.
Drets:	Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial, la distribució, i la comunicació pública de l'obra, sempre que no sigui amb finalitats comercials, i sempre que es reconegui l'autoria de l'obra original. No es permet la creació d'obres derivades.
Llengua:	Anglès
Document:	Article ; recerca ; Versió publicada
Matèria:	Precision medicine ; Data shape ; Topology ; Topological data analysis ; Persistence diagram ; Mapper ; Persistence landscapes ; Machine learning
Publicat a:	SORT : statistics and operations research transactions, Vol. 46 Núm. 1 (2022) , p. 115-136 (Articles) , ISSN 2013-8830

Adreça original: https://raco.cat/index.php/SORT/article/view/401140
DOI: 10.2436/20.8080.02.120

22 p, 1.8 MB

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