High dimensional surrogacy: computational aspects of an upscaled analysis.
J Biopharm Stat
; 30(1): 104-120, 2020.
Article
em En
| MEDLINE
| ID: mdl-31462134
ABSTRACT
Identification of genomic biomarkers is an important area of research in the context of drug discovery experiments. These experiments typically consist of several high dimensional datasets that contain information about a set of drugs (compounds) under development. This type of data structure introduces the challenge of multi-source data integration. High-Performance Computing (HPC) has become an important tool for everyday research tasks. In the context of drug discovery, high dimensional multi-source data needs to be analyzed to identify the biological pathways related to the new set of drugs under development. In order to process all information contained in the datasets, HPC techniques are required. Even though R packages for parallel computing are available, they are not optimized for a specific setting and data structure. In this article, we propose a new framework, for data analysis, to use R in a computer cluster. The proposed data analysis workflow is applied to a multi-source high dimensional drug discovery dataset and compared with a few existing R packages for parallel computing.
Palavras-chave
Texto completo:
1
Bases de dados:
MEDLINE
Assunto principal:
Projetos de Pesquisa
/
Marcadores Genéticos
/
Genômica
/
Descoberta de Drogas
Tipo de estudo:
Prognostic_studies
Limite:
Humans
Idioma:
En
Revista:
J Biopharm Stat
Assunto da revista:
FARMACOLOGIA
Ano de publicação:
2020
Tipo de documento:
Article
País de afiliação:
Bélgica