Genetic Programming as a tool for identification of analyte-specificity from complex response patterns using a non-specific whole-cell biosensor

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@Article{Podola2012254,
  author =       "Bjoern Podola and Michael Melkonian",
  title =        "Genetic Programming as a tool for identification of
                 analyte-specificity from complex response patterns
                 using a non-specific whole-cell biosensor",
  journal =      "Biosensors and Bioelectronics",
  volume =       "33",
  number =       "1",
  pages =        "254--259",
  year =         "2012",
  ISSN =         "0956-5663",
  DOI =          "doi:10.1016/j.bios.2012.01.015",
  URL =          "http://www.sciencedirect.com/science/article/pii/S0956566312000334",
  keywords =     "genetic algorithms, genetic programming, Biosensor,
                 Multisensor, Whole-cell, Microalgae, Classification,
                 Pattern recognition",
  abstract =     "Whole-cell bio-sensors are mostly non-specific with
                 respect to their detection capabilities for toxicants,
                 and therefore offering an interesting perspective in
                 environmental monitoring. However, to fully employ this
                 feature, a robust classification method needs to be
                 implemented into these sensor systems to allow further
                 identification of detected substances.
                 Substance-specific information can be extracted from
                 signals derived from biosensors harbouring one or
                 multiple biological components. Here, a major task is
                 the identification of substance-specific information
                 among considerable amounts of biosensor data. For this
                 purpose, several approaches make use of statistical
                 methods or machine learning algorithms. Genetic
                 Programming (GP), a heuristic machine learning
                 technique offers several advantages compared to other
                 machine learning approaches and consequently may be a
                 promising tool for biosensor data classification.

                 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

Citations