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Optimized cell type signatures revealed from single-cell data by combining principal feature analysis, mutual information, and machine learning
Machine learning techniques are excellent to analyze expression data from single cells. These techniques impact all fields ranging from cell annotation and clustering to signature identification. The presented framework evaluates gene selection sets how far they optimally separate defined phenotypes...
| Autores principales: | , , , , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
Research Network of Computational and Structural Biotechnology
2023
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| Materias: | |
| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10276237/ https://www.ncbi.nlm.nih.gov/pubmed/37333862 http://dx.doi.org/10.1016/j.csbj.2023.06.002 |