Our group
We apply knowledge based AI to big data bio-analytics, extracting biological insights by computational means. We employ a variety of methods from robust explainable statistical models such as Bayesian networks to developing robust well maintained pipelines that utilise the best open source algorithms and databases.
Our aims
To deliver new biological knowledge via:
- working in close collaboration with experimentalists
- getting involved early in the formulation of scientific questions
- iterative, refining processes which form a new collaborative language
- engage with experimental groups early in proposals
- properly resourcing analysis and computational tasks within bigger projects
Our research
Main areas of research include:
- knowledge based analytics
- AI, knowledge representation
- machine learning
- probabilistic logic programming
In collaboration:
- incorporate knowledge in analyses of experimental data
- interface with high quality external databases
- use and develop state-of-the-art algorithms
- well engineered robust, re-deployable pipelines
- large scale multi-omics projects
Our impact
Our methods and tools help deliver complex models of the underlying biology studied by the experimentalists at Pirbright.
Current contributions in conjunction with other Institute groups:
- single cell RNA analysis for viral spread patterns in pig lungs
- spatial transcriptomics of tracheas in viral and attenuated chicken
- sequencing, down-stream analyses from genomic datasets
- avian oncogenic viruses, differential expression in hypoxic conditions
- reference labs, automation and intelligent reporting
