Detection of Cell Types from Single-cell RNA-seq Data using Similarity via Kernel Preserving Learning Embedding-Reference-Cited by-同舟云学术

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Detection of Cell Types from Single-cell RNA-seq Data using Similarity via Kernel Preserving Learning Embedding

Published:2019-11 Issue: Volume: Page:
ISSN:
Container-title:2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)
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Author:

Liu Zeming,Liu Feng,Hong Chengzhi,Gao Meng,Chen Yi-Ping Phoebe,Liu Shichao,Zhang Wen

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/8965270/8982928/08983395.pdf?arnumber=8983395

Cited by 5 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Graph-Regularized Non-Negative Matrix Factorization for Single-Cell Clustering in scRNA-Seq Data;IEEE Journal of Biomedical and Health Informatics;2024-08

2. ScLSTM: single-cell type detection by siamese recurrent network and hierarchical clustering;BMC Bioinformatics;2023-11-07

3. Combining Global-Constrained Concept Factorization and a Regularized Gaussian Graphical Model for Clustering Single-Cell RNA-seq Data;Interdisciplinary Sciences: Computational Life Sciences;2023-10-10

4. Spectral clustering of single cells using Siamese nerual network combined with improved affinity matrix;Briefings in Bioinformatics;2022-04-13

5. Sc-GPE: A Graph Partitioning-Based Cluster Ensemble Method for Single-Cell;Frontiers in Genetics;2020-12-15

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