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Identifying homogeneous subgroups of patients and important features: a topological machine learning approach

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.
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https://hal.inria.fr/hal-03368489
Contributor : Mathieu Carrière Connect in order to contact the contributor
Submitted on : Wednesday, October 6, 2021 - 5:58:33 PM
Last modification on : Friday, July 8, 2022 - 10:09:39 AM

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Ewan Carr, Mathieu Carriere, Bertrand Michel, Frédéric Chazal, Raquel Iniesta. Identifying homogeneous subgroups of patients and important features: a topological machine learning approach. BMC Bioinformatics, 2021, 22, pp.449. ⟨10.1186/s12859-021-04360-9⟩. ⟨hal-03368489⟩

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