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

类固醇代谢的先天性疾病在出生时就可以检测到，并且可以用低成本的药物治疗。其特征是受影响婴儿尿液中特定类固醇或一组类固醇的总体增加。然而，目前还没有一种具有成本效益的方法来筛查所有这些疾病。即使在发达国家，仅筛查一种疾病（先天性肾上腺皮质增生症）的一种亚型也被认为是具有成本效益的。在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。该阵列使用基于三向连接（TWJ）的受体。化学传感器阵列设计的一个系统的程序，包括传感器合成，功能（传感器）的选择，训练数据收集，分类器的设计和分析阶段。TWJ作为传感器设计的支架，允许数千种变化，每种变化对小分子如类固醇具有不同的选择性。利用微芯片可以在固定位置合成多达90，000个传感器，从而可以对数千个传感器的响应进行全面表征。 基于包装器的特征选择方法用于从这数千个传感器子集中找到小的、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总体增加。这些浓度变化必须在存在小浓度的其他类固醇的情况下检测到，并且由于肾脏过滤的差异，样品可能会在一系列的总体稀释度上发生。这需要能够在输入浓度范围内工作的混合分类/回归推理算法。TWJ传感器具有对浓度的非线性响应，以及分析物混合物的非加性信号，这需要新的方法来进行化学传感器阵列分析和分类器设计。最后，正在开发新的基于包装的浓度覆盖程序，以确保在训练数据中准确表示传感器响应曲线，同时最大限度地减少所需的测量次数。发展中国家的绝大多数新生儿没有筛查类固醇代谢的先天性疾病;即使在美国，覆盖范围也不完整。目前的方法是精确的，但疾病特异性，昂贵，不切实际的现代医院外。TWJ传感器阵列将是廉价、稳定和可靠的;它们足够强大，可以同时测试许多类固醇代谢疾病，并可以通过异常检测来识别新的疾病。它们将有可能被部署到实地，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.