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Functional genomic hypothesis generation and experimentation by a robot scientist

King, RD, Whelan, KE, Jones, FM, Reiser, PGK, Bryant, CH, Muggleton, SH, Kell, DB and Oliver, SG 2004, 'Functional genomic hypothesis generation and experimentation by a robot scientist' , Nature, 427 (6971) , pp. 247-252.

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Abstract

The question of whether it is possible to automate the scientific process is of both great theoretical interest and increasing practical importance because, in many scientific areas, data are being generated much faster than they can be effectively analysed. We describe a physically implemented robotic system that applies techniques from artificial intelligence to carry out cycles of scientific experimentation. The system automatically originates hypotheses to explain observations, devises experiments to test these hypotheses, physically runs the experiments using a laboratory robot, interprets the results to falsify hypotheses inconsistent with the data, and then repeats the cycle. Here we apply the system to the determination of gene function using deletion mutants of yeast (Saccharomyces cerevisiae) and auxotrophic growth experiments. We built and tested a detailed logical model (involving genes, proteins and metabolites) of the aromatic amino acid synthesis pathway. In biological experiments that automatically reconstruct parts of this model, we show that an intelligent experiment selection strategy is competitive with human performance and significantly outperforms, with a cost decrease of 3-fold and 100-fold (respectively), both cheapest and random-experiment selection.

Item Type: Article
Uncontrolled Keywords: robotics, active learning, inductive logic programming
Themes: Subjects / Themes > Q Science > Q Science (General)
Subjects outside of the University Themes
Schools: Colleges and Schools > College of Science & Technology
Colleges and Schools > College of Science & Technology > School of Computing, Science and Engineering
Colleges and Schools > College of Science & Technology > School of Computing, Science and Engineering > Data Mining and Pattern Recognition Research Centre
Journal or Publication Title: Nature
Publisher: Nature Publishing Group
Refereed: Yes
ISSN: 00280836
Related URLs:
Depositing User: Dr Chris H. Bryant
Date Deposited: 16 Feb 2009 14:25
Last Modified: 20 Aug 2013 16:55
URI: http://usir.salford.ac.uk/id/eprint/1759

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