Semi-Supervised Deep Learning for Cell Type Identification From Single-Cell Transcriptomic Data

Author:

Dong Xishuang1ORCID,Chowdhury Shanta1,Victor Uboho1,Li Xiangfang1,Qian Lijun1ORCID

Affiliation:

1. Department of Electrical and Computer Engineering, Center of Excellence in Research and Education for Big Military Data Intelligence (CREDIT Center) and Center for Computational Systems Biology (CCSB), Prairie View A&M University, Texas A&M University System, Prairie View, TX, USA

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Applied Mathematics,Genetics,Biotechnology

Reference61 articles.

1. Highly Parallel Genome-wide Expression Profiling of Individual Cells Using Nanoliter Droplets

2. Cell type annotation of single-cell chromatin accessibility data via supervised Bayesian embedding

3. Recurrent Convolutional Neural Networks for Text Classification

4. DGCyTOF: Deep learning with graphic cluster visualization to predict cell types of single cell mass cytometry data;cheng;BioRxiv,2021

5. Conserved cell types with divergent features between human and mouse cortex;hodge;BioRxiv,2018

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