Overview
Availability
- Dr Chensong Chen is:
- Available for supervision
Fields of research
Qualifications
- Bachelor of Agriculture, Zhejiang A&F University
- Masters (Coursework) of Bioinformatics, The University of Queensland
- Doctor of Philosophy of Genetics, The University of Queensland
Research interests
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Deep learning for genomic prediction
Transformer and attention-based architectures for genome-wide marker data, including designs that scale to very long genotype sequences and mixture-of-experts models for additive, dominance and epistatic effects.
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Genomic selection by haplotype stacking
Haplotype-based analysis of agriculturally important genomic segments. Building and analysis haplotype catalogue, stacking algorithm and breeding simulation
Works
Search Professor Chensong Chen’s works on UQ eSpace
2026
Other Outputs
Machine learning strategies to optimize genomic prediction in polyploid sugarcane
Chen, Chensong (2026). Machine learning strategies to optimize genomic prediction in polyploid sugarcane. PhD Thesis, Queensland Alliance for Agriculture and Food Innovation, The University of Queensland. doi: 10.14264/b62bcd3
2024
Journal Article
Genomic prediction for sugarcane diseases including hybrid Bayesian-machine learning approaches
Chen, Chensong, Bhuiyan, Shamsul A., Ross, Elizabeth, Powell, Owen, Dinglasan, Eric, Wei, Xianming, Atkin, Felicity, Deomano, Emily and Hayes, Ben (2024). Genomic prediction for sugarcane diseases including hybrid Bayesian-machine learning approaches. Frontiers in Plant Science, 15 1398903, 1-13. doi: 10.3389/fpls.2024.1398903
2023
Journal Article
Genomic prediction with machine learning in sugarcane, a complex highly polyploid clonally propagated crop with substantial non‐additive variation for key traits
Chen, Chensong, Powell, Owen, Dinglasan, Eric, Ross, Elizabeth M., Yadav, Seema, Wei, Xianming, Atkin, Felicity, Deomano, Emily and Hayes, Ben J. (2023). Genomic prediction with machine learning in sugarcane, a complex highly polyploid clonally propagated crop with substantial non‐additive variation for key traits. The Plant Genome, 16 (4) e20390, 1-13. doi: 10.1002/tpg2.20390
2023
Journal Article
Advancing artificial intelligence to help feed the world
Hayes, Ben J., Chen, Chensong, Powell, Owen, Dinglasan, Eric, Villiers, Kira, Kemper, Kathryn E. and Hickey, Lee T. (2023). Advancing artificial intelligence to help feed the world. Nature Biotechnology, 41 (9), 1-2. doi: 10.1038/s41587-023-01898-2
Funding
Current funding
Supervision
Availability
- Dr Chensong Chen is:
- Available for supervision
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Media
Enquiries
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