Dr Dennis Wang

Lecturer in Bioinformatics and Genomic Medicine

Room number: 150 Regent Court
Telephone: +44 (0) 114 222 1808
Emaildennis.wang@sheffield.ac.uk

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

ORCID | Google scholar

Publications 

Dennis Wang profile photo

Profile

Biography

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 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 Deputy Theme Lead within the NIHR Sheffield Biomedical Research Centre.

Research

Research

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 building machine learning models that integrate different molecular data types to predict clinical outcome and identify biomarkers through feature selection. Of biomedical interest, he applies unsupervised segmentation methods to human genomes to better understand what differentiates patients and their response to treatments. Both of these aims enable him to work towards the ultimate goal of 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)
  • Prediction of drug combinations using machine learning (in collaboration with AstraZeneca and Sage Bionetworks)
  • Probabilistic modelling of drug response (in collaboration with Lancaster Univ, AstraZeneca, and Sanger Institute)
  • Segmenting patient populations with dementia and Alzheimer’s disease (in collaboration with the CFAS consortium)
Grants

Research grants

  • Tensor-based machine learning for personalized medicine EPSRC Centre for Doctoral Training Studentship (£65,585) PI 2018 – 2022
  • Epigenetic data integration for subtyping pulmonary hypertension Donald Heath PhD Studentship (£87,973) PI 2018 – 2022
  • Developing an in vivo CRISPR-interference Screening Resource BBSRC – Bioinformatics Resource Fund (£428,223) Co-I 2018 – 2020
  • Liquid biopsy-based screening biomarkers Weston Park Cancer Charity – Large Grant (£47,840) Co-I 2018 – 2019
  • Genomic Medicine Framework - CPPD modules along with short courses NHS Health Education England (£49,660) PI 2018 – 2019
  • Next generation clinical cancer diagnostics MRC Proximity to Discovery (£12,493) PI 2017 – 2018
  • Development of new CPD for clinical bioinformatics NHS Health Education England (£25,000) PI 2017 – 2018