Conference paper Open Access
Clinical trajectories estimated from bulk tumoral molecular proles using elastic principal trees
Alexander Chervov; Andrei Zinovyev
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.
| Name | Size | |
|---|---|---|
|
ChervovZinovyev_BulkOmics2021.pdf
md5:6ac1cae2a3b737f64a82ea67c877223a |
1.4 MB | Download |
| All versions | This version | |
|---|---|---|
| Views | 70 | 70 |
| Downloads | 31 | 31 |
| Data volume | 44.9 MB | 44.9 MB |
| Unique views | 62 | 62 |
| Unique downloads | 31 | 31 |
- Publication date:
- January 27, 2021
- DOI:
-
- Keyword(s):
- clinical trajectories, breast cancer transcriptome principal tree survival analysis
- Grants:
-
Agence Nationale de la Recherche:
- PRAIRIE - PaRis Artificial Intelligence Research InstitutE (ANR-19-P3IA-0001)
- iPC - individualizedPaediatricCure: Cloud-based virtual-patient models for precision paediatric oncology (826121)
- Meeting:
- International Joint Conference on Neural Networks-2021 (IJCNN2021), 18-22 July 2021
- Communities:
- License (for files):
- Creative Commons Attribution 4.0 International