scENT for Revealing Gene Clusters From Single-Cell RNA-Seq Data-Reference-Cited by-同舟云学术

scENT for Revealing Gene Clusters From Single-Cell RNA-Seq Data

Published:2023-05-01 Issue:3 Volume:20 Page:2266-2277
ISSN:1545-5963
Container-title:IEEE/ACM Transactions on Computational Biology and Bioinformatics
language:
Short-container-title:IEEE/ACM Trans. Comput. Biol. and Bioinf.

Author:

Rao Fan¹^ORCID,Chen Minghan²^ORCID,Yang Defu¹^ORCID,Morrell Bess²,Song Qianqian³^ORCID,Zhu Wentao¹^ORCID

Affiliation:

1. Zhejiang Lab, Hangzhou, Zhejiang, China

2. Department of Computer Science, Wake Forest University, Winston-Salem, NC, USA

3. Center for Cancer Genomics and Precision Oncology, Wake Forest Baptist Comprehensive Cancer Center, Wake Forest Baptist Medical Center, Winston Salem, NC, USA

Funder

National Natural Science Foundation of China

Key Research and Development Program of Zhejiang Province, China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Applied Mathematics,Genetics,Biotechnology

Link

http://xplorestaging.ieee.org/ielx7/8857/10144451/10044260.pdf?arnumber=10044260

Reference52 articles.

1. BABEL enables cross-modality translation between multiomic profiles at single-cell resolution

2. Integrated Single-Cell Analysis Maps the Continuous Regulatory Landscape of Human Hematopoietic Differentiation

3. Straightforward clustering of single-cell RNA-Seq data with t-SNE and DBSCAN

4. Deep cross-omics cycle attention model for joint analysis of single-cell multi-omics data

5. Clusformer: A Transformer based Clustering Approach to Unsupervised Large-scale Face and Visual Landmark Recognition

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