Shu Dong Zhang Queen’s University Belfast

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Shu Dong Zhang Queen’s University Belfast

Dr Shu-Dong Zhang

Senior Lecturer

School of Biomedical Sciences
Research Institute
Biomedical Sciences Research Institute
School of Biomedical Sciences
Ulster University
C-TRIC Building, Altnagelvin Area Hospital
Glenshane Road, Derry~Londonderry
BT47 6SB
+44 28 7167 5235
Profile image of Dr Shu-Dong Zhang

Shu-Dong Zhang initially trained as a physicist with a PhD from Beijing Normal University, specialising in non-equilibrium statistical physics and non-linear dynamics.

Before embarking on biology-oriented research, he worked on various topics in statistical physics, condensed matter physics and physical chemistry. Inspired and excited by the rapid advancement of modern biotechnologies, especially the high throughput omics technologies and the Human Genome Project, he became interested in quantitative and computational biology.

In 2002, he joined the Medical Research Council (MRC) Toxicology Unit to work side by side with biomedical scientists on transcriptomic profiling, particularly on the experimental design, methodology development, and data analysis issues associated with microarray technologies. He developed a statistical framework for designing two-colour cDNA microarray experiments, and related methods and tools to effectively detect differential gene expression. Dr Zhang investigated the effect of sample pooling in microarray experimentation, and the efficiencies and cost effectiveness of such practice. Those methods and tools have been routinely used by colleagues and collaborators in their experiments and data analysis.

Before joining the Northern Ireland Centre for Stratified Medicine, Dr Zhang worked as a Principal Investigator and a Lecturer in Bioinformatics in the Centre for Cancer Research and Cell Biology (CCRCB) at Queen’s University Belfast. One major theme of his research was to establish novel connections between diseases and various small molecule compounds, utilising high throughput transcriptomic profiling data and employing powerful and advanced Bioinformatics techniques.

He led the BBSRC/MRC/EPSRC co-funded project on gene expression connectivity mapping, to develop innovative algorithms (gene signature perturbation, gene signature progression), new software tools (sscMap, cudaMap, QUADrATiC), and novel applications of connectivity mapping to repurpose FDA-approved drugs for cancers, eg, leukaemia, colorectal cancer, and inflammatory diseases, such as cystic fibrosis.

Dr Zhang’s research also led to fruitful collaborations with biologists and cancer epidemiologists, securing joint collaborative projects funded by leading charities like Cancer Research UK. For example, applying the advanced connectivity mapping methods developed to a joint CRUK project, his work directly empowered cancer epidemiology research by providing highly promising candidate medications for population based studies with foreseeable healthcare implications.

Research publications

Showing 1 to 10 of 75 publications
To view all publications please visit Ulster University Institutional Repository (UIR)

  • The prognostic significance of Cdc6 and Cdt1 in breast cancerScientific ReportsDate: (2017)
  • The prognostic significance of DAPK1 in bladder cancerPLOS ONEDate: (2017)
  • Erythropoietin drives breast cancer progression by activation of its receptor EPOROncotargetDate: (2017)
  • CD133 in brain tumor: the prognostic factorOncotargetDate: (2017)
  • KRAS mutant colorectal cancer gene signatures identified angiotensin II receptor blockers as potential therapiesOncotargetDate: (2017)
  • Activation and cleavage of SASH1 by caspase-3 mediates an apoptotic responseCell Death and DiseaseDate: (2016)
  • SASH1 mediates sensitivity of breast cancer cells to chloropyramine and is associated with prognosis in breast cancerOncotargetDate: (2016)
  • PRL3-zumab, a first-in-class humanized antibody for cancer therapyJCI InsightDate: (2016)
  • Connectivity mapping (ssCMap) to predict A20-inducing drugs and their antiinflammatory action in cystic fibrosisProceedings of the National Academy of SciencesDate: (2016)
  • A gene-signature progression approach to identifying candidate small-molecule cancer therapeutics with connectivity mappingBMC BioinformaticsDate: (2016)