A deep learning-based unsupervised learning method for spatially resolved transcriptomic data analysis-Reference-Cited by-同舟云学术

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A deep learning-based unsupervised learning method for spatially resolved transcriptomic data analysis

Published:2022-12-06 Issue: Volume: Page:
ISSN:
Container-title:2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)
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Author:

Peng Lihong¹,He Xianzhi¹,Zhang Li²,Peng Xinhuai¹,Lu Yuankang¹,Li Zejun³,Chen Xing⁴

Affiliation:

1. Hunan University of Technology,School of Computer Science,Zhuzhou,China

2. China University of Mining and Technology,School of Information and Control Engineering,Xuzhou,China,221116

3. Artificial Intelligence Research Institute Hunan Institute of Technology,Hengyang,China

4. Artificial Intelligence Research Institute, China University of Mining and Technology,Xuzhou,China

Funder

National Natural Science Foundation of China

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9994793/9994847/09995207.pdf?arnumber=9995207

Reference29 articles.

1. The human transcriptome across tissues and individuals

2. Single-Cell RNA-Seq Reveals Hypothalamic Cell Diversity

3. A survey of human brain transcriptome diversity at the single cell level

4. Effective microtissue RNA extraction coupled with Smart-seq2 for reproducible and robust spatial transcriptome analysis

5. Uncovering an Organ’s Molecular Architecture at Single-Cell Resolution by Spatially Resolved Transcriptomics

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