PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Autonomous Experimentation: Active Learning for Enzyme Response Characterisation
Chris Lovell, Gareth Jones, Steve Gunn and Klaus-Peter Zauner
JMLR W&CP Volume To appear in volume 15, 2010.

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.

EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:7714
Deposited By:Chris Lovell
Deposited On:17 March 2011