Michael Hoffman creates predictive computational models to understand interactions between genome, epigenome, and phenotype in human cancers. His influential machine learning approaches have reshaped researchers' analysis of gene regulation. These approaches include the genome annotation method Segway, which enables simple interpretation of multivariate genomic data. He is a Senior Scientist in and Chair of the Computational Biology and Medicine Program, Princess Margaret Cancer Centre and Associate Professor in the Departments of Medical Biophysics and Computer Science, University of Toronto. He was named a CIHR New Investigator and has received several awards for his academic work, including the NIH K99/R00 Pathway to Independence Award, and the Ontario Early Researcher Award.


Changes in gene regulation often underlie the mechanism of genetic disorders and cancer. These changes can arise from variations in genomic DNA sequence. They can also come from alterations in epigenomic properties, such as DNA methylation, chromatin packaging, histone modifications, or 3D chromosome conformation. New sequencing technology reveals a forest of genomic and epigenomic variation, but we are hindered by insufficient understanding of the variation's consequences. As a result, we can apply these data to diagnosis or personalized drug therapy only in limited cases.

Our research program has three different themes, organized around addressing this gap in knowledge to understand interactions between genome, epigenome, and phenotype in human cancers.

Theme 1. Computational predictive models of gene regulation. We apply a systematic framework to create and validate predictive models of (1) how genetic variants cause epigenomic changes, and (2) the effect of epigenomic changes on gene regulation and phenotype. First, we start with data from collaborators or public resources, using cancer cell lines and cancer patient primary tissue. Second, we develop machine learning models of how a genomic or epigenomic input leads to an epigenomic or phenotypic output. Third, we perturb input data and predict changes in output. Fourth, we validate predictions with targeted experiments.

Theme 2. Epigenomic liquid biopsy. Working with several other Medical Biophysics faculty, we are developing improvements and new applications for the cell-free methylation DNA immunoprecipitation-sequencing (cfMeDIP-seq) epigenomic liquid biopsy technique developed at Princess Margaret Cancer Centre. These improvements will lead to more reliable identification of gene expression programs and diagnosis of cancer from a minimally invasive blood draw, replacing invasive tissue biopsies. We are also applying cfMeDIP-seq in new domains such as diagnosis of preterm birth disorders through maternal blood draws.

Theme 3. Robustness, reproducibility, and transparency in biological research. Like many computational biology and genomics researchers, we rely on, and contribute to, a common base of shared computational tools and data. We work to establish practices that ensure data and code are shared in ways that maximize the benefit of publicly funded research.


Related Links

For a list of Dr. Hoffman's publications, please visit PubMed, Scopus, Web of Science or ORCID.

Associate Professor, Department of Medical Biophysics, University of Toronto
Associate Professor, Department of Computer Science, University of Toronto
Faculty Affiliate, Vector Institute
Member, Collaborative Specialization in Genome Biology and Bioinformatics, University of Toronto
Member, Data Sciences Institute, University of Toronto
Member, Temerty Centre for Artificial Intelligence Research and Education in Medicine, University of Toronto
Member, PRIME, University of Toronto
Mentor, Strategic Training for Advanced Genetic Epidemiology Program, Canadian Statistical Sciences Institute Ontario