Feature Learning For Improved Multiplex Disease Diagnosis

摘要 该提案是针对CATTS的，CATTS是一种优化用于多重质谱（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，导致非常高维的特征空间， 以及随之而来的高错误发现率。CATTS旨在通过使用以下知识来减少此空间的大小： 基础生物化学，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，CATTS显着提高测定灵敏度。 这个建议采用了我们现有的CATTS实现，并对其进行了扩展，以支持额外的功能学习 算法和分类方法。此外，将测试其作为多重测定策略的性能 蛋白质和脂质MS指纹库，着眼于商业化。

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..