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I have an RAEng/EPSRC Research Fellowship to "Engineer the Intelligent Scientific Laboratory". The aim is to produce an intelligent networked automated scientific laboratory. This will integrate Semantic Web technology with the Robot Scientist project to allow intelligent use, both by humans and machines, of the information provided by the Robot Scientist and existing online bioinformatics data resources. The Robot Scientist is a project at Aberystwyth where intelligent software creates scientific hypotheses, designs experiments to distinguish between these hypotheses, controls a lab robot to conduct these experiments, and then uses the results to design the next round of experiments. There are so many aspects to the work on this project, including data formalism, experimental protocols, data collection, inference and querying, planning and scheduling, and the practicalities of working in a real lab with real automation equipment.
Previously I held an 1851 Research Fellowship to investigate Grid-enabling lab robots for the Robot Scientist. This was a two year project, Oct 2004 to Sep 2006. It would be good if lab automation manufacturers used open standards for their control interface and data formats, however, instead they currently use MS Excel, GUIs and Visual Basic scripting.
Previously, as a post doc on a BBSRC funded grant, I've been using machine learning (including ILP) and data mining (particularly multi-relational associations) in bioinformatics. My research concentrated on the area of functional genomics - elucidating the biological functions of the parts of a genome. When a genome is sequenced, and we have the predicted locations of the genes within the genome, the next stage is to work out the possible functions of these genes. We've been looking at genes in Arabidopsis thaliana, the first plant genome to be sequenced. Detailed results for Arabidopsis are available. This has involved looking at ways to make use of different kinds of data, from microarray data, sequence statistics, homology data, predicted secondary structure, QTLs, and phenotypic data. Also ways to make use of background information, hierarchical information, and also to take into account that proteins have more than one function, a classification problem where each item fits into more than one class.
I've also spent 3 months working with RMIT's Search Engine Group making a multi-relational data mining tool (Radar) based on inverted indexing.
I'm part of the Computational Biology Group in the Department of Computer Science at the Aberystwyth University.
研究兴趣
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CoRR (2024)
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CoRR (2024)
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Nucleic Acids Research (2023)
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biorxiv(2022)
Access Microbiologyno. 5 (2022)
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