Cargando…
On the Use of Topological Features of Metabolic Networks for the Classification of Cancer Samples
BACKGROUND: The increasing availability of omics data collected from patients affected by severe pathologies, such as cancer, is fostering the development of data science methods for their analysis. INTRODUCTION: The combination of data integration and machine learning approaches can provide new pow...
Autores principales: | Machicao, Jeaneth, Craighero, Francesco, Maspero, Davide, Angaroni, Fabrizio, Damiani, Chiara, Graudenzi, Alex, Antoniotti, Marco, Bruno, Odemir M. |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Bentham Science Publishers
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8188584/ https://www.ncbi.nlm.nih.gov/pubmed/34220296 http://dx.doi.org/10.2174/1389202922666210301084151 |
Ejemplares similares
-
LACE 2.0: an interactive R tool for the inference and visualization of longitudinal cancer evolution
por: Ascolani, Gianluca, et al.
Publicado: (2023) -
VERSO: A comprehensive framework for the inference of robust phylogenies and the quantification of intra-host genomic diversity of viral samples
por: Ramazzotti, Daniele, et al.
Publicado: (2021) -
Early detection and improved genomic surveillance of SARS-CoV-2 variants from deep sequencing data
por: Ramazzotti, Daniele, et al.
Publicado: (2022) -
SparseSignatures: An R package using LASSO-regularized non-negative matrix factorization to identify mutational signatures from human tumor samples
por: Mella, Lorenzo, et al.
Publicado: (2022) -
Topological assessment of metabolic networks reveals evolutionary information
por: Machicao, Jeaneth, et al.
Publicado: (2018)