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A Regularized Multi-Task Learning Approach for Cell Type Detection in Single-Cell RNA Sequencing Data
Cell type prediction is one of the most challenging goals in single-cell RNA sequencing (scRNA-seq) data. Existing methods use unsupervised learning to identify signature genes in each cluster, followed by a literature survey to look up those genes for assigning cell types. However, finding potentia...
| Autores principales: | , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
Frontiers Media S.A.
2022
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| Materias: | |
| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9043858/ https://www.ncbi.nlm.nih.gov/pubmed/35495159 http://dx.doi.org/10.3389/fgene.2022.788832 |