Cargando…
Can we infer tumor presence of single cell transcriptomes and their tumor of origin from bulk transcriptomes by machine learning?
There is a growing need to build a model that uses single cell RNA-seq (scRNA-seq) to separate malignant cells from nonmalignant cells and to identify tumor of origin of single cells and/or circulating tumor cells (CTCs). Currently, it is infeasible to build a tumor of origin model learnt from scRNA...
Autores principales: | Liu, Hua-Ping, Wang, Dongwen, Lai, Hung-Ming |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Research Network of Computational and Structural Biotechnology
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9162953/ https://www.ncbi.nlm.nih.gov/pubmed/35685355 http://dx.doi.org/10.1016/j.csbj.2022.05.035 |
Ejemplares similares
-
Single-cell transcriptomic profiling for inferring tumor origin and mechanisms of therapeutic resistance
por: Lin, Maoxuan, et al.
Publicado: (2022) -
Inferring ligand-receptor cellular networks from bulk and spatial transcriptomic datasets with BulkSignalR
por: Villemin, Jean-Philippe, et al.
Publicado: (2023) -
Inferring Multiple Sclerosis Stages from the Blood Transcriptome via Machine Learning
por: Acquaviva, Massimo, et al.
Publicado: (2020) -
CloneSig can jointly infer intra-tumor heterogeneity and mutational signature activity in bulk tumor sequencing data
por: Abécassis, Judith, et al.
Publicado: (2021) -
What can we learn from senescent platelets, their transcriptomes and proteomes?
por: Allan, Harriet E., et al.
Publicado: (2023)