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Autonomous Experimentation: Active Learning for Enzyme Response Characterisation

Record type: Article

Characterising response behaviours of biological systems is impaired by limited resources that restrict the exploration of high dimensional parameter spaces. Additionally, experimental errors that provide observations not representative of the true underlying behaviour, mean that observations obtained from these experiments cannot be regarded as always valid. To combat the problem of erroneous observations in situations where there are limited observations available to learn from, we consider the use of multiple hypotheses, where potentially erroneous observations are considered as being erroneous and valid in parallel by competing hypotheses. Here we describe work towards an autonomous experimentation machine that combines active learning techniques with computer controlled experimentation platforms to perform physical experiments. Whilst the target for our approach is the characterisation of the behaviours of networks of enzymes for novel computing mechanisms, the algorithms we are working towards remain independent of the application domain.

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Citation

Lovell, Chris, Jones, Gareth, Gunn, Steve and Zauner, Klaus-Peter (2011) Autonomous Experimentation: Active Learning for Enzyme Response Characterisation JMLR: Workshop and Conference Proceedings, 16, pp. 141-155.

More information

Published date: 21 April 2011
Organisations: Agents, Interactions & Complexity, Electronic & Software Systems

Identifiers

Local EPrints ID: 272227
URI: http://eprints.soton.ac.uk/id/eprint/272227
PURE UUID: 9ca4490a-36a9-4e15-bd44-f8c72a3ace1a

Catalogue record

Date deposited: 26 Apr 2011 11:33
Last modified: 18 Jul 2017 06:33

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Contributors

Author: Chris Lovell
Author: Gareth Jones
Author: Steve Gunn
Author: Klaus-Peter Zauner

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