ロード中...
Unsupervised generative and graph representation learning for modelling cell differentiation
Using machine learning techniques to build representations from biomedical data can help us understand the latent biological mechanism of action and lead to important discoveries. Recent developments in single-cell RNA-sequencing protocols have allowed measuring gene expression for individual cells...
保存先:
| 出版年: | Sci Rep |
|---|---|
| 主要な著者: | , , , |
| フォーマット: | Artigo |
| 言語: | Inglês |
| 出版事項: |
Nature Publishing Group UK
2020
|
| 主題: | |
| オンライン･アクセス: | https://ncbi.nlm.nih.gov/pmc/articles/PMC7300092/ https://ncbi.nlm.nih.gov/pubmed/32555334 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1038/s41598-020-66166-8 |
| タグ: |
タグ追加
タグなし, このレコードへの初めてのタグを付けませんか!
|