Zhou Xiaobo, Wang Honghui, Wang Jun, Wang Yuan, Hoehn Gerard, Azok Joseph, Brennan Marie-Luise, Hazen Stanley L, Li King, Chang Shih-Fu, Wong Stephen T C
Center for Biotechnology and Informatics, The Methodist Hospital Research Institute & Cornell University, Houston, TX 77030, USA.
Proteomics. 2009 Apr;9(8):2286-94. doi: 10.1002/pmic.200700867.
Conventional biomarker discovery focuses mostly on the identification of single markers and thus often has limited success in disease diagnosis and prognosis. This study proposes a method to identify an optimized protein biomarker panel based on MS studies for predicting the risk of major adverse cardiac events (MACE) in patients. Since the simplicity and concision requirement for the development of immunoassays can only tolerate the complexity of the prediction model with a very few selected discriminative biomarkers, established optimization methods, such as conventional genetic algorithm (GA), thus fails in the high-dimensional space. In this paper, we present a novel variant of GA that embeds the recursive local floating enhancement technique to discover a panel of protein biomarkers with far better prognostic value for prediction of MACE than existing methods, including the one approved recently by FDA (Food and Drug Administration). The new pragmatic method applies the constraints of MACE relevance and biomarker redundancy to shrink the local searching space in order to avoid heavy computation penalty resulted from the local floating optimization. The proposed method is compared with standard GA and other variable selection approaches based on the MACE prediction experiments. Two powerful classification techniques, partial least squares logistic regression (PLS-LR) and support vector machine classifier (SVMC), are deployed as the MACE predictors owing to their ability in dealing with small scale and binary response data. New preprocessing algorithms, such as low-level signal processing, duplicated spectra elimination, and outliner patient's samples removal, are also included in the proposed method. The experimental results show that an optimized panel of seven selected biomarkers can provide more than 77.1% MACE prediction accuracy using SVMC. The experimental results empirically demonstrate that the new GA algorithm with local floating enhancement (GA-LFE) can achieve the better MACE prediction performance comparing with the existing techniques. The method has been applied to SELDI/MALDI MS datasets to discover an optimized panel of protein biomarkers to distinguish disease from control.
传统的生物标志物发现主要集中在单一标志物的识别上，因此在疾病诊断和预后方面往往成效有限。本研究提出了一种基于质谱研究来识别优化蛋白质生物标志物组合的方法，用于预测患者发生主要不良心脏事件（MACE）的风险。由于免疫分析开发对简单性和简洁性的要求仅能容忍由极少数选定的有鉴别力的生物标志物构成的预测模型的复杂性，因此诸如传统遗传算法（GA）等既定的优化方法在高维空间中失效。在本文中，我们提出了一种GA的新型变体，它嵌入了递归局部浮动增强技术，以发现一组对MACE预测具有比现有方法（包括美国食品药品监督管理局（FDA）最近批准的方法）更好预后价值的蛋白质生物标志物。这种新的实用方法应用MACE相关性和生物标志物冗余性的约束来缩小局部搜索空间，以避免局部浮动优化导致的繁重计算代价。基于MACE预测实验，将所提出的方法与标准GA和其他变量选择方法进行了比较。由于偏最小二乘逻辑回归（PLS-LR）和支持向量机分类器（SVMC）这两种强大的分类技术能够处理小规模和二元响应数据，因此将它们用作MACE预测器。所提出的方法还包括新的预处理算法，如低级信号处理、重复光谱消除和去除异常患者样本。实验结果表明，使用SVMC时，由七个选定生物标志物组成的优化组合能够提供超过77.1%的MACE预测准确率。实验结果从经验上证明，与现有技术相比，具有局部浮动增强的新GA算法（GA-LFE）能够实现更好的MACE预测性能。该方法已应用于表面增强激光解吸电离/基质辅助激光解吸电离质谱（SELDI/MALDI MS）数据集，以发现一组优化的蛋白质生物标志物组合，用于区分疾病与对照。
