C. Lovell, G. Jones, S.R. Gunn &
K.-P. Zauner; JMLR W&CP 16:141–155, 2011.
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
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