Dr Boris Guennewig

Dr Boris Guennewig, Senior Lecturer, The University of Sydney.

Boris is a senior Lecturer and head of the FOREFRONT BIOINFORMATICS and STATISTICS RESEARCH GROUP.

He is a research scientist/bioinformatician/statistician specializing in the development of infrastructure, software and pipelines to manage, analyze and mine large complex datasets in medical research. Using structured, semi-structured and unstructured data, his research focuses on the identification and characterization of genetic variation and transcriptional changes influencing complex human diseases (such as frontotemporal lobe dementia, bipolar disorder, Parkinson’s and Alzheimer’s disease, etc.). He achieves this through the functional integration of high-dimensional biological (omics) data, in combination with his statistical, genetics and data mining skills. Boris believes that assimilating and modelling multi-modal data (i.e. imaging, clinical and omic data) is key to uncovering the genotype-phenotype interaction and how this relationship affects complex traits.

Forefront Group:

  • BMC Bioinformatics and Statistics Research Group Leader

Affiliate Organisations

UNSW, Garvan, Australian Genomics, International Cerebral Palsy Genetics Consortium

Neurodegeneration of interest:

All of them.


  • Bioinformatics
  • Machine learning
  • Biomarkers

Specific Skills:

  • Biostatistics
  • Bioinformatics
  • Statistics
  • HPC
  • Software development
  • data science

Project - Precision Medicine – Novel artificial intelligence approaches for the analysis of multimodal biomedical data to odentify clinically relevant biomarkers in neurogenerative diseases

Research Project Abstract

The Forefront bioinformatics and statistics group under the lead of Boris Guennewig comprises mathematicians/statisticians, IT engineers/computer scientists, and biologists/bioinformaticians at the Brain and Mind Centre, University of Sydney, specialized for development of bioinformatics & AI methodology to manage, analyze and mine large, complex datasets. The team’s contribution is the development of new methodology and the proof of concept on previously developed bioinformatics framework to process thousands of high-dimensional data sets in a time effective and coherent way.

Our research program uses artificial intelligence approaches to integrate, combine, and distil knowledge from multimodal datasets, such as high content biological data (genomics, transcriptomics, lipidomics), and functional imaging data (MRI, CT, PET) which generate invaluable insights into disease pathology. Building on our previously established computing frameworks, modular workflows, and machine learning suites that are infrastructure agnostic, we are developing innovative, reusable, reproduceable, and interoperable multimodal data analysis tools that remain viable while underlying infrastructures evolve. Unification of individual high-content data – validated against pathologically confirmed forms of the disease – will be used to identify key differentiating factors of disease pathology, determinants of progression, and biomarkers. This analytical approach can prospectively be applied to other research areas as well and will pave the way for precision medicine that takes individual variability in genes and environment into account.