Multi-omic data integration using joint non-negative matrix and machine learning methods for clinical endpoints prediction and causal parameter estimation in cancer
Currently, several data sources drive the understanding of biological or clinical processes. Although their purpose is to assist in optimal decision-making, they require strategies that facilitate these data sources¿ integration. For example, in biological sciences, multi-omic data integration has i...
- Autores:
-
Salazar Barreto, Diego Armando
- Tipo de recurso:
- Doctoral thesis
- Fecha de publicación:
- 2022
- Institución:
- Universidad de los Andes
- Repositorio:
- Séneca: repositorio Uniandes
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.uniandes.edu.co:1992/59247
- Acceso en línea:
- http://hdl.handle.net/1992/59247
- Palabra clave:
- Multi-omic integration
Kernel trick
Causal inference
Targeted Learning
Machine Learning
Glioma
Breast cancer
Lung adenocarcinoma
Drug repurposing
Precision medicine
co-clustering
Joint Non-negative Matrix Factorization
Superlearner
data fusion
Ingeniería
- Rights
- openAccess
- License
- Atribución-NoComercial 4.0 Internacional