Identifying homogeneous subgroups of patients and important features: a topological machine learning approach. Carr, E., Carrière, M., Michel, B., Chazal, F., & Iniesta, R. BMC Bioinformatics, 22(1):449, September, 2021. ![Identifying homogeneous subgroups of patients and important features: a topological machine learning approach [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex 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.
@article{carr_identifying_2021,
title = {Identifying homogeneous subgroups of patients and important features: a topological machine learning approach},
volume = {22},
issn = {1471-2105},
shorttitle = {Identifying homogeneous subgroups of patients and important features},
url = {https://doi.org/10.1186/s12859-021-04360-9},
doi = {10.1186/s12859-021-04360-9},
abstract = {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.},
number = {1},
urldate = {2021-09-22},
journal = {BMC Bioinformatics},
author = {Carr, Ewan and Carrière, Mathieu and Michel, Bertrand and Chazal, Frédéric and Iniesta, Raquel},
month = sep,
year = {2021},
keywords = {Clustering, Machine learning, Topological data analysis},
pages = {449},
}
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