Dr Dennis Wang

Senior Lecturer in Bioinformatics and Genomic Medicine

Room number: 150 Regent Court
Telephone: +44 (0) 114 222 1808

Member of the machine Learning and Natural Language Processing research groups
Personal website

ORCID | Google scholar


Dennis Wang profile photo



Dennis graduated from the University of British Columbia (Vancouver, Canada) with a BSc in Computer Science, Microbiology and Immunology. He then moved to the University of Cambridge for an MPhil in Computational Biology working on bio-networks modeling with Prof Jasmin Fisher (Microsoft Research), and a PhD in Biostatistics working on statistical analysis of epigenetics with Prof Lorenz Wernisch (MRC Biostatistics Unit).

Following the completion of his PhD in 2012, he became a postdoc and a scientific associate at the Princess Margaret Cancer Centre in Toronto working with Prof Ming-Sound Tsao and Prof Frances Shepherd on clinical genomics sequencing and medical informatics techniques. With a greater interest in drug development, he went back to Cambridge in 2014 and joined the early drug discovery division of AstraZeneca Plc. where he developed machine learning methods to identify genetic signatures that predict drug response. He joined the Sheffield Institute for Translational Neuroscience, University of Sheffield in 2016 as a Lecturer (now Senior Lecturer) of Genomic Medicine and was jointly appointed to the Dept. of Computer Science in 2018. Dennis is also the Scientific Director of the Sheffield Bioinformatics Core and Genomics Theme Lead within the NIHR Sheffield Biomedical Research Centre.



The focus of Dr Wang's research group is on improving machine recognition of patterns in genomic data to enable the development of personalised medicines that can benefit patients suffering from complex diseases.

His computational work involves work involves applying machine learning methods to medical research in order to 1) predict patient outcome and 2) identify molecular causes of disease. Both of these aims enable working towards developing data driven approaches for personalising medicine.

Examples of current projects:

  • Development of metrics for mutation burden and immune activity in patients (in collaboration with Genomics England, Personalis Inc., and Princess Margaret Hospital)
  • Probabilistic modelling of drug combinations (in collaboration with Lancaster Univ, AstraZeneca, and Sanger Institute)
  • Predicting diagnosis of pulmonary hypertension (in collaboration with Imperial and Cambridge University)
  • Segmenting patient populations with dementia and Alzheimer’s disease (in collaboration with the CFAS consortium)

Research grants

  • The Brain Tumour Charity (£119,936) Co-I 2019 – 2022
    Ex-vivo 3D models of post-surgical residual disease in glioblastoma to improve biological understanding and treatment.
  • NIHR Efficacy of Mechanism Evaluation (£815,275) Co-I 2019 – 2024
    Genotype of Ureothelial cancer: Stratified Treatment and Oncological outcomes (GUSTO).
  • AMS Springboard Award (£99,411) PI 2019 – 2021
    Evidencing subtypes of disorders through consensus of clinical and multi-omic traits.
  • Rosetrees Seedcorn Award (£30,428) PI 2019 – 2020
    Machine learning prediction of uncertainty in cancer therapy response using genomic biomarkers
  • EPSRC Centre for Doctoral Training Studentship (£65,585) PI 2018 – 2022
    Tensor-based machine learning for personalized medicine
  • Donald Heath PhD Studentship (£87,973) PI 2018 – 2022
    Epigenetic data integration for subtyping pulmonary hypertension
  • BBSRC – Bioinformatics Resource Fund (£428,223) Co-I 2018 – 2020
    Developing an in vivo CRISPR-interference Screening Resource
  • Weston Park Cancer Charity – Large Grant (£47,840) Co-I 2018 – 2019
    Liquid biopsy-based screening biomarkers
  • NHS Health Education England (£49,660) PI 2018 – 2019
    Genomic Medicine Framework - CPPD modules along with short courses