An unsupervised deep learning framework for gene regulatory network inference from single-cell expression data-Reference-Cited by-同舟云学术

An unsupervised deep learning framework for gene regulatory network inference from single-cell expression data

Published:2023-12-05 Issue: Volume: Page:2663-2670
ISSN:
Container-title:2023 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)
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Author:

Mao Guo¹,Liu Jie²

Affiliation:

1. National University of Defense Technology,Science and Technology on Parallel and Distributed Processing Laboratory,Changsha,China,410073

2. National University of Defense Technology Science and Technology on Parallel and Distributed Processing Laboratory, National University of Defense Technology,Laboratory of Software Engineering for Complex System,Changsha,China,410073

Funder

National Key Research and Development Program of China

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10385250/10385251/10385528.pdf?arnumber=10385528

Reference37 articles.

1. Gene Regulatory Network Inference: An Introductory Survey

2. Gene Regulatory Network Inference: Connecting Plant Biology and Mathematical Modeling

3. A neuro-genetic approach for inferring gene regulatory networks from gene expression data

4. Modeling gene regulatory networks using neural network architectures

5. MMFGRN: a multi-source multi-model fusion method for gene regulatory network reconstruction

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