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...

Full description

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