A genetic programming approach for Burkholderia Pseudomallei diagnostic pattern discovery

Created by W.Langdon from gp-bibliography.bib Revision:1.4192

  author =       "Zheng Rong Yang and Ganjana Lertmemongkolchai and 
                 Gladys Tan and Philip L. Felgner and Richard Titball",
  title =        "A genetic programming approach for Burkholderia
                 Pseudomallei diagnostic pattern discovery",
  journal =      "Bioinformatics",
  year =         "2009",
  volume =       "25",
  number =       "17",
  pages =        "2256--2262",
  month =        sep # " 1",
  keywords =     "genetic algorithms, genetic programming",
  DOI =          "doi:10.1093/bioinformatics/btp390",
  URL =          "http://results.ref.ac.uk/Submissions/Output/2528334",
  size =         "7 pages",
  abstract =     "MOTIVATION: Finding diagnostic patterns for fighting
                 diseases like Burkholderia pseudomallei using
                 biomarkers involves two key issues. First, exhausting
                 all subsets of testable biomarkers (antigens in this
                 context) to find a best one is computationally
                 infeasible. Therefore, a proper optimisation approach
                 like evolutionary computation should be investigated.
                 Second, a properly selected function of the antigens as
                 the diagnostic pattern which is commonly unknown is a
                 key to the diagnostic accuracy and the diagnostic
                 effectiveness in clinical use.

                 RESULTS: A conversion function is proposed to convert
                 serum tests of antigens on patients to binary values
                 based on which Boolean functions as the diagnostic
                 patterns are developed. A genetic programming approach
                 is designed for optimizing the diagnostic patterns in
                 terms of their accuracy and effectiveness. During
                 optimization, it is aimed to maximize the coverage (the
                 rate of positive response to antigens) in the infected
                 patients and minimize the coverage in the non-infected
                 patients while maintaining the fewest number of
                 testable antigens used in the Boolean functions as
                 possible. The final coverage in the infected patients
                 is 96.55percent using 17 of 215 (7.4percent) antigens
                 with zero coverage in the non-infected patients. Among
                 these 17 antigens, BPSL2697 is the most frequently
                 selected one for the diagnosis of Burkholderia
                 Pseudomallei. The approach has been evaluated using
                 both the cross-validation and the Jack-knife simulation
                 methods with the prediction accuracy as 93percent and
                 92percent, respectively. A novel approach is also
                 proposed in this study to evaluate a model with binary
                 data using ROC analysis.",
  notes =        "PMID: 19561021 [PubMed - in process]

                 PMCID: PMC2734322 [Available on 2010/09/01]",
  uk_research_excellence_2014 = "This article appears in the premier
                 journal for bioinformatics methods. I analysed the
                 data, designed, implemented and tested the algorithm. I
                 analysed the results and wrote the paper. Because the
                 data was very noisy, it was difficult to use
                 conventional approaches to identify the biomarker. I
                 then categorised the data to apply genetic programming
                 techniques to search for biomarkers. This is the first
                 time that genetic programming was applied in this
                 field. The result was validated in the wet laboratory
                 with success.",

Genetic Programming entries for Zheng Rong Yang Ganjana Lertmemongkolchai Gladys Tan Philip L Felgner Richard Titball