Data denoising with transfer learning in single-cell transcriptomics

By Jingshu Wang, Divyansh Agarwal, Mo Huang, Gang Hu, Zilu Zhou, C. Ye, Nancy R. Zhang. 2019

174 citations recorded by the source corpus.

Subfields: Representation & Self-Supervised Learning, Computational Biology & Bioinformatics

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Representation & Self-Supervised Learning

1. Marinka Zitnik
2. Jure Leskovec
3. Weihua Hu
4. Bowen Liu
5. Percy Liang
6. Vijay S. Pande
7. Joseph Gomes
8. William L. Hamilton

In Computational Biology & Bioinformatics

1. Jure Leskovec
2. Marinka Zitnik
3. Percy Liang
4. Joseph Gomes
5. Oliver Stegle
6. Vijay S. Pande
7. Bowen Liu
8. John C. Marioni

Citations in the corpus

• Strategies for Pre-training Graph Neural Networks (2019, background): “Several key studies (Xu et al., 2017; Ching et al., 2018; Wang et al., 2019) matter of increasing the number of labeled pre-training datasets that are from the same domain as the downstream task.”
• The Human Tumor Atlas Network: Charting Tumor Transitions across Space and Time at Single-Cell Resolution (2020, unknown): “, 2016b) as well as by transfer learning for training predictive models while borrowing parameters from a model that performs well on a similar task (Azizi et al., 2018; Wang et al., 2019; Yosinski et al., 2014).”
• Computational principles and challenges in single-cell data integration (2021, methodology): “Transfer learning strategies have been devised for single-cell genomics for tasks such as denoising 81 , cell type classification 82 and shared embedding 83 .”