Imputing single-cell RNA-seq data by graph autoencoder with multi-kernel-Reference-Cited by-同舟云学术

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Imputing single-cell RNA-seq data by graph autoencoder with multi-kernel

Published:2023-12-05 Issue: Volume:15 Page:228-232
ISSN:
Container-title:2023 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)
language:
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Author:

Jiang Kang¹,Liao Bo.²,Papagerakis Petros³,Wu Fang-Xiang⁴

Affiliation:

1. University of Saskatchewan,Division of Biomedical Engineering,Saskatoon,Canada

2. Hainan Normal University,School of Mathematics and Statistics,Haikou,China

3. University of Saskatchewan,College of Dentistry,Saskatoon,Canada

4. University of Saskatchewan,Division of Biomedical Engineering,Department of Mechanical Engineering,Saskatoon,Canada

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10385250/10385251/10385675.pdf?arnumber=10385675

Reference25 articles.

1. Eleven grand challenges in single-cell data science

2. A systematic evaluation of single-cell RNA-sequencing imputation methods

3. Challenges in unsupervised clustering of single-cell RNA-seq data

4. Bayesian approach to single-cell differential expression analysis

5. DrImpute: imputing dropout events in single cell RNA sequencing data

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