CITATION.cff

# YAML 1.2
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abstract: |
    "Background
    
    This paper exploits recent developments in topological data analysis to present a pipeline for clustering based on Mapper, an algorithm that reduces complex data into a one-dimensional graph.
    
    Results
    
    We present a pipeline to identify and summarise clusters based on statistically significant topological features from a point cloud using Mapper.
    
    Conclusions
    
    Key strengths of this pipeline include the integration of prior knowledge to inform the clustering process and the selection of optimal clusters; the use of the bootstrap to restrict the search to robust topological features; the use of machine learning to inspect clusters; and the ability to incorporate mixed data types. Our pipeline can be downloaded under the GNU GPLv3 license at https://github.com/kcl-bhi/mapper-pipeline."
authors: 
  -
    affiliation: "Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK"
    family-names: Carr
    given-names: Ewan
  -
    affiliation: "Inria Sophia-Antipolis, DataShape Team, Biot, France"
    family-names: "Carrière"
    given-names: Mathieu
  -
    affiliation: "Ecole Centrale de Nantes, LMJL – UMR CNRS 6629, Nantes, France"
    family-names: Michel
    given-names: Bertrand
  -
    affiliation: "Inria Saclay, Ile-de-France, Alan Turing Building, Palaiseau, France"
    family-names: Chazal
    given-names: "Frédéric"
  -
    affiliation: "Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK"
    family-names: Iniesta
    given-names: Raquel
cff-version: "1.1.0"
date-released: 2021-09-20
doi: "10.1186/s12859-021-04360-9"
keywords: 
  - "Topological data analysis"
  - Clustering
  - "Machine learning"
message: "If you use this software, please cite it using these metadata."
repository-code: "https://github.com/kcl-bhi/mapper-pipeline"
title: "Identifying homogeneous subgroups of patients and important features: a topological machine learning approach"
...