A genetic programming approach for Burkholderia pseudomallei diagnostic pattern discovery.
A genetic programming approach for Burkholderia pseudomallei diagnostic pattern discovery.
复制标题
用于发现鼻疽伯克霍尔德氏菌诊断模式的遗传编程方法。
DOI：
10.1093/bioinformatics/btp390
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发表时间：
2009
期刊：
影响因子：
--
通讯作者：
Titball,Richard
中科院分区：
文献类型：
--
作者：
Yang,ZhengRong;Lertmemongkolchai,Ganjana;Tan,Gladys;Felgner,PhilipL;Titball,Richard
Motivation:Finding diagnostic patterns for fighting diseases likeBurkholderia pseudomalleiusing 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 optimization 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 96.55% using 17 of 215 (7.4%) antigens with zero coverage in the non-infected patients. Among these 17 antigens, BPSL2697 is the most frequently selected one for the diagnosis ofBurkholderia Pseudomallei. The approach has been evaluated using both the cross-validation and the Jack–knife simulation methods with the prediction accuracy as 93% and 92%, respectively. A novel approach is also proposed in this study to evaluate a model with binary data using ROC analysis.Contact:z.r.yang@ex.ac.uk
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