10.5281/zenodo.5782816
https://zenodo.org/records/5782816
oai:zenodo.org:5782816
Alexander Chervov
Alexander Chervov
Institut Curie
Andrei Zinovyev
Andrei Zinovyev
Institut Curie
Clinical trajectories estimated from bulk tumoral molecular proles using elastic principal trees
Zenodo
2021
clinical trajectories,
breast cancer
transcriptome
principal tree
survival analysis
2021-01-27
eng
10.5281/zenodo.5782815
https://zenodo.org/communities/ipc
https://zenodo.org/communities/eu
Creative Commons Attribution 4.0 International
Clinical trajectory is a clinically relevant sequence of ordered patient phenotypes representing consecutive states of a developing disease and leading to some final state. Extracting trajectories from large scale medical data is of great interest for dynamical phenotyping of various diseases but remains a challenge for machine learning methods, especially in the case of synchronic (with short follow up) observations. Here we describe an approach for trajectory-based analysis of cancer data using elastic principal trees and test it on a large collection of molecular tumoral profiles for breast cancer. We show that the disease progress quantified with pseudotime (the geodesic distance from the root) along a particular trajectory can serve as a significant prognostic factor, not redundant with gene expression-based predictors. We conclude that application of the elastic principal trees to transcriptomic data can be of interest for clinical applications.
European Commission
10.13039/501100000780
826121
individualizedPaediatricCure: Cloud-based virtual-patient models for precision paediatric oncology
Agence Nationale de la Recherche
10.13039/501100001665
ANR-19-P3IA-0001
PaRis Artificial Intelligence Research InstitutE