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...
Saved in:
Main Authors: | Ken Asada, Ken Takasawa, Hidenori Machino, Satoshi Takahashi, Norio Shinkai, Amina Bolatkan, Kazuma Kobayashi, Masaaki Komatsu, Syuzo Kaneko, Koji Okamoto, Ryuji Hamamoto |
---|---|
Format: | article |
Language: | EN |
Published: |
MDPI AG
2021
|
Subjects: | |
Online Access: | https://doaj.org/article/98e97ae928d2494d80ad06936e7c2a72 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Simultaneous amplicon analysis of multiple soil samples using MinION sequencing
by: Hiroyuki Kurokochi, et al.
Published: (2021) -
Genetic Analysis of Chinese Patients with Early-Onset Dementia Using Next-Generation Sequencing
by: Han LH, et al.
Published: (2020) -
RBPSpot: Learning on appropriate contextual information for RBP binding sites discovery
by: Nitesh Kumar Sharma, et al.
Published: (2021) -
A Portrait of Intratumoral Genomic and Transcriptomic Heterogeneity at Single-Cell Level in Colorectal Cancer
by: Andrea Angius, et al.
Published: (2021) -
Evaluation of CRISPR Diversity in the Human Skin Microbiome for Personal Identification
by: Kochi Toyomane, et al.
Published: (2021)