Research

Projects - Publications - Datasets - Other

Projects

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.

Publications

• Alsberg, B. and Clare, A. (2010) Wiki based management of chemometric research projects. Journal of Chemometrics, in press
• Sparkes, A., Aubrey, W., Byrne, E., Clare, A., Khan, M. N., Liakata, M., Markham, M., Rowland, J., Soldatova, L. N., Whelan, K. E., Young, M. and King, R. D. (2010) Towards Robot Scientists for autonomous scientific discovery. Automated Experimentation 2010, 2:1 doi:10.1186/1759-4499-2-1
• Sparkes, A., King, R. D., Aubrey, W., Benway, M., Byrne, E., Clare, A., Liakata, M., Markham, M., Whelan, K. E., Young, M., Rowland, J. (2010) An Integrated Laboratory Robotic System for Autonomous Discovery of Gene Function JALA 15(1) pages 33-40.
• King, R. D., Rowland, J., Aubrey, W., Liakata, M., Markham, M., Soldatova, L. N., Whelan, K. E., Clare, A., Young, M., Sparkes, A., Oliver, S. G., Pir, P. (2009) The Robot Scientist Adam, IEEE Computer, vol. 42, no. 8, pp. 46-54, August, doi:10.1109/MC.2009.270
• King, R. D., Rowland, J., Oliver, S. G., Young, M., Aubrey, W., Byrne, E., Liakata, M., Markham, M., Pir, P., Soldatova, L. N., Sparkes, A., Whelan, K. E., Clare, A. (2009) The Automation of Science. Science 324(5923):85-89, 3rd April 2009. (preprint pdf, before final corrections)
• Soldatova, L., Aubrey, W., King, R. D. and Clare, A. (2008) The EXACT description of biomedical protocols. Bioinformatics 2008 24: i295-i303. Special issue for ISMB 2008. See also EXACT webpage.
• Riley, M.C., Clare, A. and King, R. D. (2007) Locational distribution of gene functional classes in Arabidopsis thaliana BMC Bioinformatics 8:112
• Blockeel, H., Schietgat, L., Struyf, J., Dzeroski, S., Clare, A. (2006) Decision Trees for Hierarchical Multilabel Classification: A Case Study in Functional Genomics. In proceedings of PKDD 2006.
• Soldatova, L., Clare, A., Sparkes, A. and King, R. D. (2006) An ontology for a robot scientist. Bioinformatics 2006 22: 464-471. Also in ISMB 2006. Archived in CADAIR here.
• Clare, A., Karwath, A., Ougham, H. and King, R. D. (2006) Functional Bioinformatics for Arabidopsis thaliana. Bioinformatics 2006 22: 1130-1136
• Struyf, J., Dzeroski, S. Blockeel, H. and Clare, A. (2005) Hierarchical Multi-classification with Predictive Clustering Trees in Functional Genomics. In proceedings of the EPIA 2005 CMB Workshop. Springer link
• Clare, A. (2005) Integration of genomic and phenotypic data. Data Analysis and Visualization in Genomics and Proteomics, Eds. Francisco Azuaje and Joaquin Dopazo, Wiley, London. ISBN: 0-470-09439-7
• Clare, A., Williams, H. E. and Lester, N. (2004) Scalable multi-relational association mining. In proceedings of the 4th IEEE International Conference on Data Mining (ICDM '04). p355-358. abstract
• King, R. D., Wise, P. H. and Clare, A. (2004) Confirmation of Data Mining Based Predictions of Protein Function. Bioinformatics 20(7) 1110-1118, abstract, genepredictions.org
• Clare, A. and King, R. D. (2003) Predicting gene function in Saccharomyces cerevisiae. ECCB 2003 (published as a journal supplement in Bioinformatics 19: ii42-ii49, abstract
• Clare, A. (2003) Machine learning and data mining for yeast functional genomics. PhD thesis. University of Wales Aberystwyth. pdf (1Mb) This was a runner-up in the 2004 BCS Distinguished Dissertations Award.
• Clare, A. and King R.D. (2003) Data mining the yeast genome in a lazy functional language. In Practical Aspects of Declarative Languages (PADL'03) (won Best/Most Practical Paper award), abstract, pdf
• Clare, A. and King R.D. (2002) How well do we understand the clusters found in microarray data? In Silico Biol. 2, 0046, abstract, html, further data
• Clare, A. and King R.D. (2002) Machine learning of functional class from phenotype data. Bioinformatics 18(1) 160-166. abstract, gzipped postscript, pdf, further data
• Clare, A. and King R.D. (2001) Knowledge Discovery in Multi-Label Phenotype Data. In proceedings of ECML/PKDD 2001. abstract, gzipped postscript, pdf, further data, code
• King, R.D., Karwath, A., Clare, A., & Dehaspe, L. (2001) The Utility of Different Representations of Protein Sequence for Predicting Functional Class. Bioinformatics 17(5) 445-454. abstract, gzipped pdf, further data
• King, R.D., Karwath, A., Clare, A., & Dehapse, L. (2000) Accurate prediction of protein functional class in the M. tuberculosis and E. coli genomes using data mining. Comparative and Functional Genomics 17 283-293 (nb: volume 1 of CFG was volume 17 of Yeast). actual article, gzipped preprint postscript, further data
• King, R.D., Karwath, A., Clare, A., & Dehapse, L. (2000) Genome scale prediction of protein functional class from sequence using data mining. In: The Sixth International Conference on Knowledge Discovery and Data Mining (KDD 2000). gzipped pdf, further data
• Rose, T., Elworthy, D., Kotcheff, A., Clare, A., Tsonis, P. (2000) ANVIL: a system for the retrieval of captioned images using NLP techniques. In Challenge of Image Retrieval, Brighton, 2000. gzipped doc

Data sets

• E. coli, relational (at UCI KDD)
• TB, relational (at UCI KDD)
• E. coli and TB (our site)
• Phenotype data (yeast)
• Various data (yeast) - sequence, phenotype, expression, homology, secondary structure
• See also results, predictions, software etc here.

Other Stuff - mostly old

• For entertainment, download the department's ProteinMusic program (as featured on S4C and in The Times!), though it's getting pretty old now.
• See the ILP 2005 Challenge
• Notes on How to make Haskell code run faster and smaller by making it stricter, though some of this is becoming old and out of date now with the recent activity from the Haskell community.
• An example of how to use Castor to marshall and unmarshall XML files using 3 namespaces:
  plain text instructions castorReadme.txt and gzipped tarfile castorNSexample.tar.gz (this example created with help from Paul Wise and Irene Schneider, and bugfix by Tim Pizey)

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