About Us

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    Our lab has established a biological big data scientific research platform to conduct molecular genetic research on human diseases, covering multiple omics fields such as genomics, transcriptomics, proteomics, and metabolomics.
    Our lab uses changes in genetic information in cells to learn more about how cancer develops, especially in hepatocellular carcinoma and pancreatic neuroendocrine tumor. Cells are ultimately controlled by the genetic information they hold. Scientists call this information a cell’s genome. We already know that the genome of a cancer cell is always different to the genome of a healthy cell. It accumulates changes that eventually lead to cancer. Our lab looks at genome changes in different types of cancer to learn more about the genetic changes that lead to tumours.
    Another research interest for our lab is to understand the genetic basis of coronary heart disease using large-scale genetic data, including sequencing data in candidate genes, whole-genome genotyping data, and whole-exome sequencing data.
    Our lab has published 17 papers in the genetics of coronary heart disease, including 1) The study of the patterns of variation in the FH causative gene, PCSK931; and an evolution model of coronary heart disease; 2) The genetic risk score for coronary heart disease and its use in the reclassification of 10 years coronary heart disease risk; 3) genome-wide association studies on cardiovascular-related quantitative traits.

Office manager:Keyue Ding

Keyue Ding, Ph.D. Professor Department of Bioinformatics,
Chongqing Medical University, Chongqing, China 1# Yixueyuan Road, Yuzhong District, Chongqing, 400016 P.R. China
Email: ding.keyue@igenetics.org.cn

Research interests are molecular genetics of human diseases, established biological big data research platform, which is mainly engaged in molecular genetics research of human diseases, covering multiple omics fields such as genomics, transcriptomics, proteomics and metabolomics. Has been published more than 40 papers in international authoritative journals such as Circulation, Molecular Biology and Evolution, American Journal of Human Genetics, Bioinformatics. The article has been cited more than 2,100 times, and the H-index is 22. Major achievements include:
1) Development of genetic data analysis software; 2) methodology research and software development of human genome variation patterns; 3) analysis of mutation patterns of genes susceptible to coronary heart disease; 4) genome-wide association study of human red blood cell traits; 5) linkage localization of risk factors for coronary heart disease; and 6) translational medicine related research.

What we do

Subcellular proteomics

The purpose of subcellular proteomics is to map the location of all proteins in the cell, so that the intracellular tissue can be systematically observed. The power of comparative spatial proteomics has begun as a discovery tool to reveal disease mechanisms.

Machine learning

At present, machine learning is widely studied in the field of life science and is the mainstream research method of bioinformatics. Our laboratory is committed to the application of machine learning to a variety of research directions, including neoantigens prediction and sequencing, natural language processing, and methods for distinguishing the origins of liver cancer tumors by MRI images.

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Developing...

Students



Publication

Feb 19, 2020.

DNA and RNA sequencing identified a novel oncogene VPS35 in liver hepatocellular carcinoma

A graph clustering of the transcribed tumor-mutated alleles characterized overlapped functional clusters, and thus prioritized potentially novel oncogenes. We validated the function of the potentially novel oncogenes in vitro and in vivo. We showed that a component of the retromer complex—the vacuolar protein sorting-associated protein 35 (VPS35)— promoted the proliferation of hepatoma cell through the PI3K/AKT signaling pathway. In VPS35-knockout hepatoma cells, a significantly reduced distribution of membrane fibroblast growth factor receptor 3 (FGFR3) demonstrated the effects of VPS35 on sorting and trafficking of transmembrane receptor.

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Aug 14, 2018.

Evolutionary Genetics of Coronary Heart Disease

we discuss the current hypotheses and knowledge about the evolutionary genetics of several risk factors for CHD. Several candidate genes in pathways of blood pressure, glucose and lipid metabolism, blood coagulation, and inflammation that may be under natural selection are enumerated. An evolutionary perspective might explain why contemporary humans are at high risk for CHD and also helps to better understand variation in disease susceptibility.

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Apr , 2020.

Using natural language processing to extract clinically useful information from Chinese electronic medical records

A NLP system was developed for clinical information extraction and HCC staging based on EMRs, and the results indicate that Chinese NLP has potential utility in clinical research.

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