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Combining inductive logic programming, active learning and robotics to discover the function of genes

Bryant, CH, Muggleton, SH, Kell, DB, Reiser, P, King, RD and Oliver, SG 2001, 'Combining inductive logic programming, active learning and robotics to discover the function of genes' , Electronic Transactions in Artificial Intelligence, 5 (B) , pp. 1-36.

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    Abstract

    The paper is addressed to AI workers with an interest in biomolecular genetics and also to biomolecular geneticists interested in what AI tools may do for them. The authors are engaged in a collaborative enterprise aimed at partially automating some aspects of scientific work. These aspects include the processes of forming hypotheses, devising trials to discriminate between these competing hypotheses, physically performing these trials and then using the results of these trials to converge upon an accurate hypothesis. As a potential component of the reasoning carried out by an "artificial scientist" this paper describes ASE-Progol, an Active Learning system which uses Inductive Logic Programming to construct hypothesised first-order theories and uses a CART-like algorithm to select trials for eliminating ILP derived hypotheses. In simulated yeast growth tests ASE-Progol was used to rediscover how genes participate in the aromatic amino acid pathway of Saccharomyces cerevisiae. The cost of the chemicals consumed in converging upon a hypothesis with an accuracy of around 88% was reduced by five orders of magnitude when trials were selected by ASE-Progol rather than being sampled at random. While the naive strategy of always choosing the cheapest trial from the set of candidate trials led to lower cumulative costs than ASE-Progol, both the naive strategy and the random strategy took significantly longer to converge upon a final hypothesis than ASE-Progol. For example to reach an accuracy of 80%, ASE-Progol required 4 days while random sampling required 6 days and the naive strategy required 10 days.

    Item Type: Article
    Themes: Subjects / Themes > Q Science > QA Mathematics > QA075 Electronic computers. Computer science
    Subjects / Themes > Q Science > QH Natural history > QH301 Biology
    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: Electronic Transactions in Artificial Intelligence
    Publisher: Royal Swedish Academy of Sciences
    Refereed: Yes
    ISSN: 14033534
    Related URLs:
    Depositing User: Dr Chris H. Bryant
    Date Deposited: 16 Feb 2009 14:56
    Last Modified: 20 Aug 2013 16:56
    URI: http://usir.salford.ac.uk/id/eprint/1760

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