Hsu Wen-Chin, Denq Christopher, Chen Su-Shing
Systems Biology Lab, University of Florida, Gainesville, FL, 32611, USA.
J Clin Bioinforma. 2013 Apr 25;3(1):9. doi: 10.1186/2043-9113-3-9.
Like all other neurodegenerative diseases, Alzheimer's disease (AD) remains a very challenging and difficult problem for diagnosis and therapy. For many years, only historical, behavioral and psychiatric measures have been available to AD cases. Recently, a definitive diagnostic framework, using biomarkers and imaging, has been proposed. In this paper, we propose a promising diagnostic methodology for the framework.
In a previous paper, we developed an efficient SVM (Support Vector Machine) based method, which we have now applied to discover important biomarkers and target networks which provide strategies for AD therapy.
The methodology selects a number of blood-based biomarkers (fewer than 10% of initial numbers on three AD datasets from NCBI), and the results are statistically verified by cross-validation. The resulting SVM is a classifier of AD vs. normal subjects. We construct target networks of AD based on MI (mutual information). In addition, a hierarchical clustering is applied on the initial data and clustered genes are visualized in a heatmap. The proposed method also performs gender analysis by classifying subjects based on gender.
Unlike other traditional statistical analyses, our method uses a machine learning-based algorithm. Our method selects a small set of important biomarkers for AD, differentiates noisy (irrelevant) from relevant biomarkers and also provides the target networks of the selected biomarkers, which will be useful for diagnosis and therapeutic design. Finally, based on the gender analysis, we observe that gender could play a role in AD diagnosis.
与所有其他神经退行性疾病一样，阿尔茨海默病（AD）在诊断和治疗方面仍然是一个极具挑战性和困难的问题。多年来，对于AD病例，仅有基于病史、行为和精神方面的测量方法。最近，一种使用生物标志物和成像技术的确定性诊断框架被提出。在本文中，我们为该框架提出了一种有前景的诊断方法。
在之前的一篇论文中，我们开发了一种基于支持向量机（SVM）的高效方法，现在我们将其应用于发现重要的生物标志物和目标网络，这些生物标志物和目标网络为AD治疗提供策略。
该方法选择了一些基于血液的生物标志物（在来自NCBI的三个AD数据集中，所选数量少于初始数量的10%），并且通过交叉验证对结果进行了统计学验证。所得的支持向量机是AD与正常受试者的分类器。我们基于互信息构建AD的目标网络。此外，对初始数据应用层次聚类，并在热图中可视化聚类的基因。所提出的方法还通过按性别对受试者进行分类来进行性别分析。
与其他传统统计分析不同，我们的方法使用基于机器学习的算法。我们的方法为AD选择了一小部分重要的生物标志物，区分了噪声（无关）生物标志物和相关生物标志物，还提供了所选生物标志物的目标网络，这将有助于诊断和治疗设计。最后，基于性别分析，我们观察到性别可能在AD诊断中起作用。
