CDI-Type I: Collaborative Research: Supervised Learning in Molecular Classifiers

先天的类固醇代谢疾病在出生时就可以检测到，并可以用低成本的药物治疗。它们的特征是受影响婴儿的尿液中特定类固醇或一组类固醇明显增加。然而，目前还没有成本效益高的方法来筛查所有这些疾病。即使在发达国家，只筛查一种疾病的一种亚型(先天性肾上腺增生症)也被认为是具有成本效益的。在这个CDI项目中，正在开发能够廉价、强大和可靠地筛查类固醇代谢疾病的化学传感器阵列。这些阵列使用基于寡核苷酸的受体，称为三向连接(TWJ)。采用了一个系统的化学传感器阵列设计程序，包括传感器综合、特征(传感器)选择、训练数据收集以及分类器设计和分析等阶段。TWJ充当传感器设计的脚手架，允许数千种变化，每一种对类固醇等小分子具有不同的选择性。使用微芯片可以在固定位置合成多达90,000个传感器，从而实现对数千个传感器响应的全面表征。基于包装器的特征选择方法被用来从这数千个传感器中寻找小的、高质量的传感器子集。诊断决策需要检测和量化特定指示性类固醇浓度的显著增加。这些浓度的变化必须在存在小浓度其他类固醇的情况下被检测到，并且由于肾脏滤过的不同，样本可能发生在一系列总体稀释度上。这要求混合分类/回归推理算法能够在一系列输入浓度上工作。TWJ传感器对浓度和分析物混合物的信号具有非线性响应，这就需要采用新的方法来进行化学传感器阵列分析和分类器设计。最后，正在开发新的基于Wapper的浓度覆盖程序，以确保在训练数据中准确表示传感器响应曲线，同时将所需的测量次数降至最低。发展中国家的绝大多数新生儿没有进行类固醇代谢先天疾病的筛查；即使在美国，覆盖范围也不完整。目前的方法是精确的，但针对疾病，昂贵，而且在现代医院外不切实际。TWJ传感器阵列将是廉价、稳定和可靠的；它们足够强大，可以同时检测多种类固醇代谢性疾病，并可以通过异常检测识别新的疾病。它们将有可能在实地部署，从而在发达国家对许多罕见疾病进行具有成本效益的筛查，并首次在世界其他地区进行具有成本效益的筛查。

Inborn diseases of steroid metabolism are detectable at birth and treatable with low-cost medicine. They are characterized by a gross increase of a specific steroid or set of steroids in the urine of affected infants. At present, however, there is no cost-effective method for screening for all such diseases. Even in developed countries, screening for only one subtype of only one such disease (congenital adrenal hyperplasia) is considered cost-effective.In this CDI project, chemical sensor arrays are being developed that are capable of cheap, powerful, and reliable screening for diseases of steroid metabolism. The arrays use oligonucleotide-based receptors known as three-way junctions (TWJs). A systematic procedure for chemical sensor array design is used, covering the phases of sensor synthesis, feature (sensor) selection, training data collection, and classifier design and analysis. The TWJ acts as a scaffold for sensor design, allowing thousands of variations, each with a different selectivity for small molecules such as steroids. Comprehensive characterization of thousands of sensor responses is made possible with microchips that can synthesize up to 90,000 sensors at fixed locations. Wrapper-based feature selection approaches are used to find small, high-quality sensor subsets from these thousands.Diagnostic decisions require detecting and quantifying gross increases in concentrations of particular indicative steroids. These concentration changes must be detected in the presence of small concentrations of other steroids, and, owing to differences in kidney filtrations, samples may occur over a range of overall dilutions. This requires mixed classification/regression inference algorithms capable of working over a range of input concentrations. TWJ sensors have non-linear responses to concentration, and non-additive signals for analyte mixtures, and this requires new approaches to chemical sensor array analysis and classifier design. Lastly, new wrapper-basedconcentration coverage procedures are being developed to ensure accurate representation of sensor response profiles in training data while minimizing the number of measurements needed.The vast majority of newborns in developing countries are not screened for inborn illnesses of steroid metabolism; even in the US the coverage is not complete. Current methods are precise but disease-specific, expensive, and impractical outside a modern hospital. TWJ sensor arrays will be cheap, stable, and reliable; they are powerful enough to test for many steroid metabolic diseases simultaneously, and can identify new diseases via anomaly detection. They will have the potential to be deployed in the field, resulting in cost-effective screening of many rare diseases in developed countries, and, for the first time, cost-effective screening in the rest of the world as well.