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

CDI-I 型：协作研究：分子分类器中的监督学习

• 批准号: 1027877
• 负责人: Darko Stefanovic
• 金额: $ 25万
• 依托单位: University of New Mexico
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-10-01 至 2014-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1027877&HistoricalAwards=false
• 关键词: CDI; Type; Collaborative; Research; Supervised

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.

类固醇代谢的先天性疾病在出生时就可以检测出来，并且可以用低成本药物治疗。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的总体增加。然而，目前还没有一种经济有效的方法来筛查所有此类疾病。即使在发达国家，仅筛查一种此类疾病（先天性肾上腺增生）的一种亚型也被认为具有成本效益。在这个 CDI 项目中，正在开发化学传感器阵列，能够廉价、强大且可靠地筛查疾病类固醇代谢。该阵列使用基于寡核苷酸的受体，称为三路连接（TWJ）。使用化学传感器阵列设计的系统程序，涵盖传感器合成、特征（传感器）选择、训练数据收集以及分类器设计和分析的阶段。 TWJ 充当传感器设计的支架，允许数千种变化，每种变化对类固醇等小分子具有不同的选择性。微芯片可以在固定位置合成多达 90,000 个传感器，从而可以全面表征数千个传感器响应。 基于包装的特征选择方法用于从这数千个传感器中找到小型、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。必须在存在小浓度其他类固醇的情况下检测这些浓度变化，并且由于肾脏过滤的差异，样品可能会出现在一定范围的总体稀释度中。这需要能够在一系列输入浓度下工作的混合分类/回归推理算法。 TWJ 传感器对浓度具有非线性响应，并且对分析物混合物具有非加性信号，这需要新的化学传感器阵列分析和分类器设计方法。最后，正在开发新的基于包装的浓度覆盖程序，以确保训练数据中传感器响应曲线的准确表示，同时最大限度地减少所需的测量数量。发展中国家的绝大多数新生儿没有接受类固醇代谢先天性疾病的筛查；即使在美国，覆盖范围也不完整。目前的方法很精确，但针对特定疾病，价格昂贵，并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；它们的功能足够强大，可以同时测试多种类固醇代谢疾病，并且可以通过异常检测来识别新疾病。它们将有潜力在现场部署，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。