Bioinformatics and Imaging
Professor Peter Hamilton, Lead Investigator
The Cancer Bioinformatics and Imaging group consists of scientists with expertise across a broad spectrum of subjects including Computational Biology, Computer Vision, Machine Learning, Data Integromics, Systems and Network Biology.
The group is leading on high-throughput big data analysis in cancer genomics, molecular oncology and tissue pathology for molecular diagnostics and precision medicine.
With a focus on solid cancers, they provide a vital key in deciphering the complex genomic and phenotypic landscape of cancer, and in identifying prognostic and predictive biomarkers. This is strongly allied to the rapid developments in molecular pathology and the translation of new genomic and tissue-based tests into practice. Digital pathology, image analysis and tissue informatics provide important technologies to support high throughput computerised analysis of solid tumour tissue samples and to understand the complex interplay between genotype and phenotype.
The aim of the group is to lead internationally on the development of novel computational and statistical methods in the analysis of genomic and image data, and to support interdisciplinary collaborative research by working closely together with biologists, oncologists and pathologists within the Centre for Cancer Research and Cell Biology (CCRCB).
Areas of interest include:
- Next generation sequencing and genomics;
- Data integromics and PICAN;
- Tissue imaging, QuPath and immuno-oncology algorithms;
- Educating the next generation of bioinformaticians.
Key research areas include:
- Computational biology and biostatistics;
- Pathway analysis, causal inference of regulatory networks, and integration of genetics and genomics data;
- Digital pathology, tissue imaging, image analysis and tissue biomarker discovery;
- High-throughput analysis of genomic and image data;
- Quantitative methods in linking genomes to targeted therapeutic compounds;
- Data “Integromics”;
- The development of new computational methods and their application to translational cancer research.