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

先天性类固醇代谢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。它们的特点是受影响婴儿尿液中某种或某种类固醇的总量增加。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，仅筛查一种此类疾病（先天性肾上腺增生症）的一种亚型也被认为具有成本效益。在这个CDI项目中，化学传感器阵列正在开发，能够廉价、强大和可靠地筛选类固醇代谢疾病。该阵列使用基于寡核苷酸的受体，称为三向连接（twj）。使用了一个系统的化学传感器阵列设计程序，包括传感器合成，特征（传感器）选择，训练数据收集和分类器设计和分析的阶段。TWJ作为传感器设计的支架，允许数千种变化，每一种对小分子（如类固醇）有不同的选择性。对数千个传感器响应的综合表征是可能的，微芯片可以在固定位置合成多达90,000个传感器。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、高质量的子集。诊断决定需要检测和量化特定指示性类固醇浓度的总增加。这些浓度变化必须在存在小浓度其他类固醇的情况下检测到，并且由于肾滤过的差异，样品可能在一定范围的总体稀释范围内发生。这需要混合分类/回归推理算法能够在输入浓度范围内工作。TWJ传感器对分析物混合物的浓度和非加性信号具有非线性响应，这需要化学传感器阵列分析和分类器设计的新方法。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概况，同时最大限度地减少所需的测量次数。发展中国家绝大多数新生儿没有接受先天性类固醇代谢疾病筛查；即使在美国，覆盖范围也不完整。目前的方法是精确的，但针对特定的疾病，价格昂贵，而且在现代医院之外不切实际。TWJ传感器阵列将廉价、稳定、可靠；它们足够强大，可以同时检测许多类固醇代谢疾病，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.