- 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.",
