Statistical Genetics Modelling: Quantitative genetics models for phenotype prediction and causal gene discovery.
Systems Biology Modelling: Molecular mechanisms using genome-scale metabolite networks and other biological networks.
Artificial Intelligence in Genetics: Machine learning / deep learning models for genetic architecture and environmental perturbation.
Multi-Omics Data Analysis: Integration of genomic data with intermediate molecular data to comprehensively interpret biological processes.
Ph.D. in Systems Biology, Max-Planck-Institute of Molecular Plant Physiology & University of Potsdam, 2019
M.Sc. in Genetics, National Key Laboratory of Crop Genetic Improvement & Huazhong Agricultural University, 2015
B.Sc. in Biotechnology, Huazhong Agricultural University, 2012
Hao Tong, Anika Küken, Zoran Nikoloski (2020).
Integrating molecular markers into metabolic models improves genomic selection for Arabidopsis growth.
Nature Communications, 11:2410.
Hao Tong, Zoran Nikoloski (2020).
Machine learning approaches for crop improvement: Leveraging phenotypic and genotypic big data.
Journal of Plant Physiology, 257:153354.
Hao Tong, Anika Küken, Zahra Razaghi-Moghadam, Zoran Nikoloski (2021).
Characterization of effects of genetic variants via genome-scale metabolic modelling.
Cellular and Molecular Life Sciences, 78, 5123–5138.
Zhu F, Alseekh S, Koper K, Tong H, Nikoloski Z, Naake T, Liu H, Yan J, Brotman Y, Wen W, Maeda H, Cheng Y, Fernie A R.
Genome-wide association of the metabolic shifts underpinning dark-induced senescence in Arabidopsis
The Plant Cell, 2022 Jan; 34 (1), 557–578
Tong H, Nankar AN, Liu J, Todorova V, Ganeva D, Grozeva S, Tringovska I, Pasev G, Radeva-Ivanova V, Gechev T, Kostova D, Nikoloski Z.
Genomic prediction of morphometric and colorimetric traits in Solanaceous fruits.
Horticulture Research, Volume 9, 2022, uhac072