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..

抽象的 该建议是针对Catts的，这是一种优化用于多重质谱（MS）的特征学习技术 指纹测定。 MS指纹由大量化学物种组成，导致非常高的特征空间， 以及随后的高误发现率。 CATT旨在通过使用知识来减少该空间的大小 潜在的生物化学以及通用聚类算法。 我们的初步结果表明，当用作各种分类的特征学习技术时 方法，CATT可显着提高测定敏感性。 该建议采用我们现有的CATT实施，并将其扩展以支持其他功能学习 算法和分类方法。此外，将测试其作为多重测定策略的性能 在蛋白质和脂质MS指纹文库上，着眼于商业化。