Bioinformatics and Imaging
Professor Peter Hamilton, Lead Investigator
The Cancer Bioinformatics (CBI) group forms part of the Clinical Research Division. The group consists of scientists with expertise across a broad spectrum ranging from Computational Biology, Computer Vision and Machine Learning to Systems and Network Biology.
In the era of high-throughput data, quantitative methods are key for elucidating biological processes. For complex diseases like cancer the deciphering of molecular signatures and networks for diagnostic and treatment modalities form major challenges for translational and experimental cancer research. The aim of the group is to develop novel computational and statistical methods and to engage in interdisciplinary collaborative research by working closely together with biologists and clinicians across the CCRCB, providing the interface between data and understanding.
Developing innovative research programmes in Cancer Bioinformatics is a priority of the team. Key research areas include:
- Computational Biology and Biostatistics;
- Pathway analysis, causal inference of regulatory networks and integration of genetics and genomics data;
- Tissue Imaging, Analytics and Biomarker Discovery;
- High-throughput analysis of genomic and image data;
- Quantitative methods in disease-genes-drugs connection discovery;
- Biomolecular Structure Prediction;
- Data integration.
The research of the group spans a wide range from basic research and method development to their applications. The group has specific interests in drug resistance and various types of complex diseases like lung cancer, colorectal cancer, cervical cancer and haematological malignancies.
In addition, the group takes a leading role in the education and mentoring of students and scientists to provide them with a deeper knowledge and understanding of modern quantitative methods as needed to cope with the data revolution in biology and medicine. Furthermore, the group aims to generate a public awareness of the current exiting developments in quantitative cancer research.