Topological Data Analysis and its usefulness for precision medicine studies

• Raquel Iniesta ^[1] ; Ewan Carr ^[1] ; Mathieu Carrière ^[4] ; Naya Yerolemou ^[2] ; Bertrand Michel ^[3] ; Frédéric Chazal ^[5]
  1. [1] King's College London

     King's College London

     Reino Unido

     
  2. [2] University of Oxford

     University of Oxford

     Oxford District, Reino Unido

     
  3. [3] Ecole Centrale de Nantes

     Ecole Centrale de Nantes

     Arrondissement de Nantes, Francia

     
  4. [4] Inria Sophia-Antipolis, DataShape Team, Biot, Francia
  5. [5] Inria Saclay - Ile-de-France, Francia

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• Localización: Sort: Statistics and Operations Research Transactions, ISSN 1696-2281, Vol. 46, Nº. 1, 2022, págs. 115-136
• Idioma: inglés
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• Resumen

  • 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.