We, through algorithm development for analyzing multi-dimensional big health data and nutrition/dietary data, aim to:
1. discern subtypes of complex diseases (through system identification of comorbidity patterns and multi-omics data analysis), parse their underlying genetic bases, and design subtype-specific precision-nutrition interventions;
2. describe disease co-occurrence patterns, build risk prediction models, and search for the optimal combinations of diets and (or) drugs;
3. elucidate the tripartite relationship among human digestive systems, microbiota therein, and food nutrition.
Guangwen Luo, Email: luoguangwen@caas.cn
Associate Researcher at the Food Genomics Centre, AGIS.
Bioinformatician (bacterial genome, metagenome, single-cell RNAseq, CNV, phenome, and metabolome)
Research direction
Based on multi-omics data, we apply bioinformatics technologies to discern biological mechanisms, and then design solutions for precision nutrition intervention
•Construct subtypes of cardiometabolic diseases and digestive tract diseases to improve the resolution of disease stratification
• Describe the characteristics of digestive tract diseases and cardiometabolic diseases, analyze their genetic, microbiome, and dietary risk factors, and explain the interaction between food nutrition, the human digestive system, and the microbial community
• Human pathogen detection and in vitro diagnostic early screening
Career
2020-2022, Department of Pathology, The University of Hong Kong
Position: Postdoctoral Fellow
2019-2020, Department of Medicine and Therapeutics, The Chinese University of Hong Kong
Position: Research Assistant
2010-2019, Precision Health Research Institute, BGI research
Highest Position: Associate Research Fellow
Education
2013-2019, Northeast Agricultural University, PhD of Food Science
2007-2010, Sichuan University, Master of Pharmaceutical Engineering
2003-2007, Sichuan University, Bachelor of Pharmaceutical Engineering
Wenjuan Yu, Email: yuwenjuan@caas.cn
Yu's main research interests are biobank data mining, tumor growth mechanism models, human biology and time series data analysis, and disease trajectory prediction. Familiar with traditional, machine learning, and deep learning approaches, can build mechanisms or data-driven models.
Andy Zhu
Zhaonian Dong
Xuhui Zhu
Wenrui Liu
Bofan Xiao
Xinnan Wu
Xuntian Rong
Chunrong Chen
Tianxin Xu
Jing Zhao
Ziyu Li
Jiabao Li
Xinru Shi
Yunhua Xie
Ruocheng Zhang
Jing Liu
Shuxia Hao
Jiasong He