Feature Learning For Improved Multiplex Disease Diagnosis

用于改进多种疾病诊断的特征学习

• 批准号: 9813275
• 负责人: ERIK J NILSSON
• 金额: $ 2.53万
• 依托单位: DEURION, LLC
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-12-01 至 2019-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9813275
• 关键词: Algorithms; Antimicrobial Resistance; Biochemistry; Biodiversity; Biological Assay; Chemicals; Classification; Clinical Microbiology; Conflict (Psychology); Data Set; Decision Trees; Detection; Development; Dimensions; Eye; Fingerprint; Immune; Knowledge; Learning; Libraries; Lipids; Machine Learning; Mass Spectrum Analysis; Measures; Methods; Modeling; Noise; Organism; Pathogen detection; Patients; Performance; Process; Protein Fingerprints; Proteins; Public Health; Reporting; Research; Sampling; Statistical Methods; Structure; System; Techniques; Testing; Work; base; commercialization; disease diagnosis; feeding; high dimensionality; improved; learning algorithm; learning strategy; neural network; novel; random forest; vector

Abstract This proposal is for CATTS, a feature learning technique optimized for use in multiplex mass spectrometry (MS) fingerprinting assays. MS fingerprints consist of a large number of chemical species, leading to very high dimensional feature spaces, and subsequent high false-discovery rates. CATTS aims to reduce the size of this space, by using knowledge of the underlying biochemistry, as well as general-purpose clustering algorithms. Our preliminary results demonstrate that, when used as a feature-learning technique for a variety of classification methods, CATTS significantly improves assay sensitivity. This proposal takes our existing implementation of CATTS and extends it to support additional feature learning algorithms and classification methods. Additionally, its performance as a multiplex assay strategy will be tested on both protein and lipid MS fingerprint libraries, with an eye towards commercialization..

摘要 这项建议是针对CATTS的，这是一种优化用于多路质谱仪(MS)的特征学习技术 指纹分析。 MS指纹由大量的化学物种组成，导致了非常高维的特征空间， 以及随之而来的高误发率。CATS的目标是通过使用以下知识来减小此空间的大小 基础生物化学，以及通用的聚类算法。 我们的初步结果表明，当作为一种特征学习技术用于各种分类时 方法：CATTS显著提高了检测灵敏度。 该提案采用了我们现有的CATTS实现，并对其进行了扩展以支持其他功能学习 算法和分类方法。此外，还将测试其作为多重检测策略的性能 蛋白质和脂肪的MS指纹库，着眼于商业化。