De-Shuang Huang
De-Shuang Huang is a researcher who applies neural networks and deep learning to biological data. The International Association for Pattern Recognition named them a 2014 fellow. In 2022 they co-authored a deep learning framework for base-resolution prediction of transcription factor binding signals, and the paper listed them at Tongji University in Shanghai. Their 2024 PLOS Computational Biology paper and their 2025 PLOS paper both listed them at the Eastern Institute of Technology in Ningbo. As of October 2026, they work at the Eastern Institute of Technology in Ningbo.
Timeline
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2014
A pattern recognition fellowship
The International Association for Pattern Recognition names them a 2014 fellow for contributions to neural networks and biological data processing.
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2022
Base-resolution binding predictions
In 2022, PLOS Computational Biology publishes their co-authored deep learning framework for base-resolution prediction of transcription factor binding signals. The paper lists them at Tongji University in Shanghai.
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2024
GMean for TF binding sites
In 2024, Scientific Reports publishes GMean, a semi-supervised GRU and K-mean model for predicting TF binding sites, which they co-wrote.
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2024
Predicting circRNA-RBP sites
PLOS Computational Biology publishes iCRBP-LKHA in 2024, a model using a large convolutional kernel and hybrid channel-spatial attention to identify circRNA-RBP interaction sites. The paper lists them at the Eastern Institute for Advanced Study, Eastern Institute of Technology, Ningbo.
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2025
Spatial transcriptome domains
A 2025 PLOS Computational Biology paper, co-written by them, introduces SpaMWGDA, a multi-view graph convolutional method for identifying spatial domains in spatial transcriptomes.
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As of October 2026
Eastern Institute of Technology, Ningbo
They work at the Eastern Institute of Technology in Ningbo, the affiliation given on their November 2025 PLOS paper.
Sources and credits
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- IAPR Fellows for 2014 – International Association for Pattern Recognition
- Base-resolution prediction of transcription factor binding signals by a deep learning framework | PLOS Computational Biology
- GMean—a semi-supervised GRU and K-mean model for predicting the TF binding site | Scientific Reports
- iCRBP-LKHA: Large convolutional kernel and hybrid channel-spatial attention for identifying circRNA-RBP interaction sites | PLOS Computational Biology
- SpaMWGDA: Identifying spatial domains of spatial transcriptomes using multi-view weighted fusion graph convolutional network and data augmentation | PLOS Computational Biology