Created by W.Langdon from gp-bibliography.bib Revision:1.7546
In the present study, we have evaluated the use of GP for the classification of herbicides and herbicide classes (chemical classes) by analysis of substance-specific patterns derived from a whole-cell multi-species biosensor. We re-analysed data from a previously described array-based biosensor system employing diverse microalgae (Podola and Melkonian, 2005), aiming on the identification of five individual herbicides as well as two herbicide classes. GP analyses were performed using the commercially available GP software `Discipulus', resulting in classifiers (computer programs) for the binary classification of each individual herbicide or herbicide class.
GP-generated classifiers both for individual herbicides and herbicide classes were able to perform a statistically significant identification of herbicides or herbicide classes, respectively. The majority of classifiers were able to perform correct classifications (sensitivity) of about 80-95percent of test data sets, whereas the false positive rate (specificity) was lower than 20percent for most classifiers. Results suggest that a higher number of data sets may lead to a better classification performance.
In the present paper, GP-based classification was combined with a biosensor for the first time. Our results demonstrate GP was able to identify substance-specific information within complex biosensor response patterns and furthermore use this information for successful toxicant classification in unknown samples. This suggests further research to assess perspectives and limitations of this approach in the field of biosensors.",
Genetic Programming entries for Bjoern Podola Michael Melkonian