AI and Computational Biology
The CRUK Scotland Institute uses advanced genomics and imaging technologies to capture multimodal data describing tumour cells and their environment. Research groups across the Institute are applying a variety of multimodal data fusion approaches to integrate and analyse these data.
A burgeoning computational biology theme combines AI and computational research with a programme of work to provide the platform infrastructure required to manage, share, and compute over high volumes of complex multimodal data.
Supporting Infrastructure
SPLICE (Scientific Platform for Life Sciences data Integration, Collaboration and Exploration) SPLICE is a well-curated data platform supporting standardised and transparent data capture, pre-processing and QA. It makes data available within a secure computational framework with access to the GPU-accelerated machine cycles required to analyse them.
AI accelerated compute On premises compute is provided by a local GPU-enable HPC system. We routinely access national supercomputing infrastructure including STFC DiRAC, ISAMBARD-AI and EPCC Archer 2.
Training and careers
Research at the Institute is highly interdisciplinary, highly collaborative and led by PIs from bench-, clinical-, and computational backgrounds. Parallel career paths support the development of research scientists, IT professionals and software engineers.
Our internship scheme with STFC DiRAC supports scientists from the physics and astronomy community interested in applying their numerical skills to challenging problems in cancer research. With multiple groups active in the AI and data science space, we support and mentor scientists from a numerical background who wish to apply their skills to the complexities of cancer biology, through all career stages, from graduate student to senior PI.
Research Groups
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Philip Dunne Phenotypic Plasticity in Colorectal Cancer
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Xiao Fu Integrative Modelling
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Ross Gray Data Science and Data Management
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Ramanuj DasGupta Cancer Systems Biology and Tumour Evolution
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John Le Quesne Deep Phenotyping
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Shahlini Rao Transcriptional and Epigenetic Control in Cancer
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Ralitsa Madsen Cell State Control of Oncogenic Signalling
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Crispin Miller Computational Biology
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Owen Sansom Colorectal Cancer and Wnt Signalling
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Ke Yuan AI for Cancer Research