An adaptive genetic algorithm for selection of blood-based biomarkers for prediction of Alzheimer's disease progression

BMC Bioinformatics

Research

Luke Vandewater¹  William Wilson¹  Ping Zhang²  Lance Macaulay³  Vladimir Brusic⁴ 

[1] Digital Productivity Flagship, CSIRO, Australia;Digital Productivity Flagship, CSIRO, Australia;Menzies Health Institute Queensland, Griffith University, Australia;Food and Nutrition Flagship, CSIRO, Australia;School of Medicine and Bioinformatics Center, Nazarbayev University, Kazakhstan;

关键词: adaptive genetic algorithm;    logistic regression;    Alzheimer's;    biomarkers;    prediction;   

DOI  :  10.1186/1471-2105-16-S18-S1

来源: Springer

【 摘 要 】

BackgroundAlzheimer's disease is a multifactorial disorder that may be diagnosed earlier using a combination of tests rather than any single test. Search algorithms and optimization techniques in combination with model evaluation techniques have been used previously to perform the selection of suitable feature sets. Previously we successfully applied GA with LR to neuropsychological data contained within the The Australian Imaging, Biomarkers and Lifestyle (AIBL) study of aging, to select cognitive tests for prediction of progression of AD. This research addresses an Adaptive Genetic Algorithm (AGA) in combination with LR for identifying the best biomarker combination for prediction of the progression to AD.ResultsThe model has been explored in terms of parameter optimization to predict conversion from healthy stage to AD with high accuracy. Several feature sets were selected - the resulting prediction moddels showed higher area under the ROC values (0.83-0.89). The results has shown consistency with some of the medical research reported in literature.ConclusionThe AGA has proven useful in selecting the best combination of biomarkers for prediction of AD progression. The algorithm presented here is generic and can be extended to other data sets generated in projects that seek to identify combination of biomarkers or other features that are predictive of disease onset or progression.

【 授权许可】

Unknown   
© Vandewater et al. 2015. This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.