Smccnet 2.0: a comprehensive tool for multi-omics network inference with shiny visualization.
BMC Bioinformatics
; 25(1): 276, 2024 Aug 24.
Article
em En
| MEDLINE
| ID: mdl-39179997
ABSTRACT
Sparse multiple canonical correlation network analysis (SmCCNet) is a machine learning technique for integrating omics data along with a variable of interest (e.g., phenotype of complex disease), and reconstructing multi-omics networks that are specific to this variable. We present the second-generation SmCCNet (SmCCNet 2.0) that adeptly integrates single or multiple omics data types along with a quantitative or binary phenotype of interest. In addition, this new package offers a streamlined setup process that can be configured manually or automatically, ensuring a flexible and user-friendly experience. AVAILABILITY:
This package is available in both CRAN https//cran.r-project.org/web/packages/SmCCNet/index.html and Github https//github.com/KechrisLab/SmCCNet under the MIT license. The network visualization tool is available at https//smccnet.shinyapps.io/smccnetnetwork/ .Palavras-chave
Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
Software
/
Aprendizado de Máquina
Limite:
Humans
Idioma:
En
Ano de publicação:
2024
Tipo de documento:
Article