Single-cell classification using graph convolutional networks

Single-cell classification using graph convolutional networks

Background: Analyzing single-cell RNA sequencing (scRNAseq) data plays an important role in understanding the intrinsic and extrinsic cellular processes in biological and biomedical research. One significant effort in this area is the identification of cell types. With the availability of a huge amo...

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Bibliographic Details
Main Authors: Bai, J. (Author), Nabavi, S. (Author), Wang, T. (Author)
Format: Article
Language:English
Published: BioMed Central Ltd 2021
Subjects:
article
Biomedical research
Cell classification
Classification (of information)
Classification accuracy
Convolution
Convolutional networks
convolutional neural network
Convolutional neural network
Convolutional neural networks
Cytology
deep learning
Deep learning
gene expression
Gene expression
Gene Expression Data
gene interaction
Gene interaction networks
Gene interactions
gene regulatory network
Gene Regulatory Networks
Graph convolutional neural network
Learning systems
machine learning
Machine Learning
Machine learning classification
Neural Networks, Computer
single cell RNA seq
Single cell RNA sequencing
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