Biology Reference
In-Depth Information
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FIGURE 2.13
The sigmoid function.
data were labelled with, for example, 0 for proteins with a cytoplasmic location and 1
for secreted proteins, and set of variables which led to an output less than 0.5 would
be classified as having a cytoplasmic location, while any with an output of more than
0.5 would be classified as secreted. It can be demonstrated that a neural network can
learn any function with arbitrary precision but, as so often the case with machine
learning algorithms, the selection of requisite architecture and training regime is a
“black art”, usually addressed using trial and error.
Neural networks can be a very powerful approach to classification and model-
ling of complex systems. They do require a lot of data; one rule of thumb is that
there should be at least five cases in the training set for each weight in the network.
Dataset size is, however, often a moot point with microbiological data. Input data
can be real valued, categorical or binary, making ANNs a flexible approach to
many problems. One criticism often directed at ANNs is that they are “black
boxes”. Unlike classifiers such as decision trees, it is not easy (although not impos-
sible) to untangle the relative importance of the variables that contribute to the
classification.
Neural networks have been used in microbiology to address a very wide range of
problems, such as the estimation of hydrogen production by genetically modified
E. coli ( Rosales-Colunga et al. , 2010 ); optimization of biomolecule production
( Singh et al. , 2009; Nelofer et al. , 2012 ); predicting bacterial community assem-
blages ( Larsen et al. , 2012 ); elucidating the relationship between growth and envi-
ronmental factors in Staphylococcus aureus ( Fern ´ ndez-Navarro et al. , 2010 );
optimising fermentation conditions for E. coli ( Silva et al. , 2012 ) and predicting
essential genes in microbial genomes ( Palaniappan and Mukherjee, 2011 ) (this
research also incorporated SVMs and decision trees). There have even been attempts
to build ANNs using microbes ( Ozasa et al. , 2009 ).
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