Single-Cell Analysis Using Machine Learning Techniques and Its Application to Medical Research
In recent years, the diversity of cancer cells in tumor tissues as a result of intratumor heterogeneity has attracted attention. In particular, the development of single-cell analysis technology has made a significant contribution to the field; technologies that are centered on single-cell RNA seque...
Guardado en:
Autores principales: | Ken Asada, Ken Takasawa, Hidenori Machino, Satoshi Takahashi, Norio Shinkai, Amina Bolatkan, Kazuma Kobayashi, Masaaki Komatsu, Syuzo Kaneko, Koji Okamoto, Ryuji Hamamoto |
---|---|
Formato: | article |
Lenguaje: | EN |
Publicado: |
MDPI AG
2021
|
Materias: | |
Acceso en línea: | https://doaj.org/article/98e97ae928d2494d80ad06936e7c2a72 |
Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
-
Simultaneous amplicon analysis of multiple soil samples using MinION sequencing
por: Hiroyuki Kurokochi, et al.
Publicado: (2021) -
Genetic Analysis of Chinese Patients with Early-Onset Dementia Using Next-Generation Sequencing
por: Han LH, et al.
Publicado: (2020) -
RBPSpot: Learning on appropriate contextual information for RBP binding sites discovery
por: Nitesh Kumar Sharma, et al.
Publicado: (2021) -
A Portrait of Intratumoral Genomic and Transcriptomic Heterogeneity at Single-Cell Level in Colorectal Cancer
por: Andrea Angius, et al.
Publicado: (2021) -
Evaluation of CRISPR Diversity in the Human Skin Microbiome for Personal Identification
por: Kochi Toyomane, et al.
Publicado: (2021)