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

Autor: Ewan Carr, Mathieu Carrière, Bertrand Michel, Frédéric Chazal, Raquel Iniesta

Publikováno v: BMC Bioinformatics, Vol 22, Iss 1, Pp 1-7 (2021)

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 ident

Externí odkaz: https://doaj.org/article/7987bfc4f24c4d468de684aefd90a4f1

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

Autor: Mathieu Carrière, Frédéric Chazal, Raquel Iniesta, Ewan Carr, Bertrand Michel

Publikováno v: BMC Bioinformatics, Vol 22, Iss 1, Pp 1-7 (2021)
BMC Bioinformatics
BMC Bioinformatics, BioMed Central, 2021
BMC Bioinformatics, 2021, 22, pp.449. ⟨10.1186/s12859-021-04360-9⟩

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 s

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::56cf112f5067a269a9ceeee7a870e7f4
https://doaj.org/article/7987bfc4f24c4d468de684aefd90a4f1

Topological Uncertainty: Monitoring trained neural networks through persistence of activation graphs

Autor: Marc Glisse, Yuhei Umeda, Yuichi Ike, Frédéric Chazal, Théo Lacombe, Mathieu Carrière

Publikováno v: IJCAI 2021-International Joint Conference on Artificial Intelligence
IJCAI 2021-International Joint Conference on Artificial Intelligence, Aug 2021, Montréal, Canada
IJCAI
IJCAI 2021-30th International Joint Conference on Artificial Intelligence
IJCAI 2021-30th International Joint Conference on Artificial Intelligence, Aug 2021, Montréal, Canada. ⟨10.24963/ijcai.2021/367⟩

International audience; Although neural networks are capable of reaching astonishing performances on a wide variety of contexts, properly training networks on complicated tasks requires expertise and can be expensive from a computational perspective.

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::064eb62294b724918cb045b7a547bb28
https://hal.archives-ouvertes.fr/hal-03213188

Topological data analysis identifies emerging adaptive mutations in SARS-CoV-2

Autor: Mathieu Carrière, Raul Rabadan, Ulrich Bauer, Andreas Ott, Lukas Hahn, Juan Ángel Patiño-Galindo, Michael Bleher

The COVID-19 pandemic has lead to a worldwide effort to characterize its evolution through the mapping of mutations in the genome of the coronavirus SARS-CoV-2. As the virus spreads and evolves it acquires new mutations that could have important publ

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::d80538fd5dba936d09bb77d1933dbcde
https://doi.org/10.1101/2021.06.10.21258550

Structure and Stability of the One-Dimensional Mapper

Autor: Mathieu Carrière, Steve Oudot

Publikováno v: Foundations of Computational Mathematics. 18:1333-1396

Given a continuous function $$f:X\rightarrow \mathbb {R}$$ and a cover $$\mathcal {I}$$ of its image by intervals, the Mapper is the nerve of a refinement of the pullback cover $$f^{-1}(\mathcal {I})$$ . Despite its success in applications, little is

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::72f963226175ca09bee8dc11aca1b5ca
https://doi.org/10.1007/s10208-017-9370-z