Proteomic mass spectra classification using decision tree based ensemble methods
Proteomic mass spectra classification using decision tree based ensemble methods
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DOI：
10.1093/bioinformatics/bti494
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发表时间：
2005-07-15
期刊：
影响因子：
5.8
通讯作者：
Wehenkel, L
中科院分区：
文献类型：
--
作者：
Geurts, P;Fillet, M;Wehenkel, L
动机：现代质谱法可以确定体液（如血清、唾液或尿液）的蛋白质组指纹。这些测量可以用于许多医疗应用，以诊断当前状态或预测疾病的演变。机器学习的最新发展允许人们利用这样的数据集，其特征在于少量的非常高维的samples.Results：我们提出了一个系统的方法，基于决策树集成方法，这是用来自动确定蛋白质组生物标志物和预测模型。该方法在两个表面增强激光解吸/电离飞行时间测量数据集上进行了验证，用于诊断类风湿性关节炎和炎症性肠病。结果表明，该方法可以处理广泛的类类似的问题。
Motivation: Modern mass spectrometry allows the determination of proteomic fingerprints of body fluids like serum, saliva or urine. These measurements can be used in many medical applications in order to diagnose the current state or predict the evolution of a disease. Recent developments in machine learning allow one to exploit such datasets, characterized by small numbers of very high-dimensional samples.Results: We propose a systematic approach based on decision tree ensemble methods, which is used to automatically determine proteomic biomarkers and predictive models. The approach is validated on two datasets of surface-enhanced laser desorption/ionization time of flight measurements, for the diagnosis of rheumatoid arthritis and inflammatory bowel diseases. The results suggest that the methodology can handle a broad class of similar problems.