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Autonomous Experimentation: Active Learning for Enzyme Response Characterisation

Autonomous Experimentation: Active Learning for Enzyme Response Characterisation
Autonomous Experimentation: Active Learning for Enzyme Response Characterisation
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.
141-155
Lovell, Chris
1ac8eed7-512f-4082-a7ab-75b5e4950518
Jones, Gareth
469d05ca-944e-43cd-91bc-12074c13848e
Gunn, Steve
306af9b3-a7fa-4381-baf9-5d6a6ec89868
Zauner, Klaus-Peter
c8b22dbd-10e6-43d8-813b-0766f985cc97
Lovell, Chris
1ac8eed7-512f-4082-a7ab-75b5e4950518
Jones, Gareth
469d05ca-944e-43cd-91bc-12074c13848e
Gunn, Steve
306af9b3-a7fa-4381-baf9-5d6a6ec89868
Zauner, Klaus-Peter
c8b22dbd-10e6-43d8-813b-0766f985cc97

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, 141-155.

Record type: Article

Abstract

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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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: 14 Mar 2024 09:50

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Contributors

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

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