Dr Dennis Wang
Lecturer in Bioinformatics and Genomic Medicine
Room number: 150 Regent Court
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.
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: