Salekin Sirajul, Bari Mehrab Ghanat, Raphael Itay, Forsthuber Thomas G, Zhang Jianqiu Michelle
Department of Electrical and Computer Engineering, University of Texas at San Antonio, One UTSA Circle, San Antonio, TX 78207, USA.
Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, 200 First Street SW, MN, Rochester, 55905, USA.
BMC Bioinformatics. 2017 Jun 23;18(1):313. doi: 10.1186/s12859-017-1712-y.
Identifying disease correlated features early before large number of molecules are impacted by disease progression with significant abundance change is very advantageous to biologists for developing early disease diagnosis biomarkers. Disease correlated features have relatively low level of abundance change at early stages. Finding them using existing bioinformatic tools in high throughput data is a challenging task since the technology suffers from limited dynamic range and significant noise. Most existing biomarker discovery algorithms can only detect molecules with high abundance changes, frequently missing early disease diagnostic markers.
We present a new statistic called early response index (ERI) to prioritize disease correlated molecules as potential early biomarkers. Instead of classification accuracy, ERI measures the average classification accuracy improvement attainable by a feature when it is united with other counterparts for classification. ERI is more sensitive to abundance changes than other ranking statistics. We have shown that ERI significantly outperforms SAM and Localfdr in detecting early responding molecules in a proteomics study of a mouse model of multiple sclerosis. Importantly, ERI was able to detect many disease relevant proteins before those algorithms detect them at a later time point.
ERI method is more sensitive for significant feature detection during early stage of disease development. It potentially has a higher specificity for biomarker discovery, and can be used to identify critical time frame for disease intervention.
在大量分子因疾病进展而出现显著丰度变化之前尽早识别疾病相关特征，这对生物学家开发早期疾病诊断生物标志物非常有利。疾病相关特征在早期阶段的丰度变化水平相对较低。在高通量数据中使用现有的生物信息学工具来发现这些特征是一项具有挑战性的任务，因为该技术存在动态范围有限和显著噪声的问题。大多数现有的生物标志物发现算法只能检测到具有高丰度变化的分子，经常会遗漏早期疾病诊断标志物。
我们提出了一种名为早期反应指数（ERI）的新统计量，用于将疾病相关分子优先排序为潜在的早期生物标志物。ERI衡量的不是分类准确率，而是一个特征与其他特征联合用于分类时可实现的平均分类准确率提高。ERI对丰度变化比其他排序统计量更敏感。我们已经表明，在一项多发性硬化症小鼠模型的蛋白质组学研究中，ERI在检测早期反应分子方面显著优于SAM和Localfdr。重要的是，ERI能够在那些算法在较晚时间点检测到之前就检测到许多与疾病相关的蛋白质。
ERI方法在疾病发展早期对显著特征的检测更敏感。它在生物标志物发现方面可能具有更高的特异性，并且可用于确定疾病干预的关键时间框架。
