Dataset Information

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 .

SUBMITTER: Carr E

PROVIDER: S-EPMC8451168 | biostudies-literature | 2021 Sep

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

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

Carr Ewan E Carrière Mathieu M Michel Bertrand B Chazal Frédéric F Iniesta Raquel R

BMC bioinformatics 20210920 1

<h4>Background</h4>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.<h4>Results</h4>We present a pipeline to identify and summarise clusters based on statistically significant topological features from a point cloud using Mapper.<h4>Conclusions</h4>Key strengths of this pipeline include the integration of prior knowledge to inform the clustering process an ...[more]

PMID: 34544357

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Dataset Information

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

Background

Results

Conclusions

Publications

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

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