scMIC: A Deep Multi-Level Information Fusion Framework for Clustering Single-Cell Multi-Omics Data-Reference-Cited by-同舟云学术

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scMIC: A Deep Multi-Level Information Fusion Framework for Clustering Single-Cell Multi-Omics Data

Published:2023-12 Issue:12 Volume:27 Page:6121-6132
ISSN:2168-2194
Container-title:IEEE Journal of Biomedical and Health Informatics
language:
Short-container-title:IEEE J. Biomed. Health Inform.

Author:

Zhan Youlin¹^ORCID,Liu Jiahan¹^ORCID,Ou-Yang Le¹^ORCID

Affiliation:

1. State Key Laboratory of Radio Frequency Heterogeneous Integration, Guangdong Key Laboratory of Intelligent Information Processing, Shenzhen Key Laboratory of Media Security, Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ), College of Electronics and Information Engineering, Shenzhen University, Shenzhen, China

Funder

National Natural Science Foundation of China

Basic and Applied Basic Research Foundation of Guangdong Province

Shenzhen Science and Technology Program

(Key) Project of Department of Education of Guangdong Province

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Health Information Management,Electrical and Electronic Engineering,Computer Science Applications,Health Informatics

Link

http://xplorestaging.ieee.org/ielx7/6221020/10345388/10255079.pdf?arnumber=10255079

Reference56 articles.

1. Single-cell RNA-seq: advances and future challenges

2. Effective Clustering of scRNA-seq Data to Identify Biomarkers without User Input

3. RNA-Seq and human complex diseases: recent accomplishments and future perspectives

4. Computational analysis of cell-to-cell heterogeneity in single-cell RNA-sequencing data reveals hidden subpopulations of cells

5. Inference of differentiation time for single cell transcriptomes using cell population reference data

Cited by 2 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. Clustering single-cell multi-omics data via graph regularized multi-view ensemble learning;Bioinformatics;2024-03-28

2. MOINER: A Novel Multiomics Early Integration Framework for Biomedical Classification and Biomarker Discovery;Journal of Chemical Information and Modeling;2024-02-19

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