Cargando…
DiviK: divisive intelligent K-means for hands-free unsupervised clustering in big biological data
BACKGROUND: Investigating molecular heterogeneity provides insights into tumour origin and metabolomics. The increasing amount of data gathered makes manual analyses infeasible—therefore, automated unsupervised learning approaches are utilised for discovering tissue heterogeneity. However, automated...
| Autores principales: | , |
|---|---|
| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
BioMed Central
2022
|
| Materias: | |
| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9743550/ https://www.ncbi.nlm.nih.gov/pubmed/36503372 http://dx.doi.org/10.1186/s12859-022-05093-z |