APPLYING INFORMATION THEORY TO NEURAL NETWORK ANALYSIS OF 1H NMR SPECTRA

这个项目试图更好地理解神经网络是如何 操作以自动显示新收集的 光谱到相应数据库的搜索引擎。我们有 开发了一种新的方法来使用神经网络来表示 从根本上改变了目前的方法。带着新的 方法论化学特征(光谱)可以呈现 直接发送到神经网络，而不进行任何预处理 降低噪音、修正基线或消除污染物的步骤 并且分类精度不会有任何损失。这项技术已经 已成功应用于CCRC-Net系统中的搜索开发 低聚木糖和小N-链的引擎 低聚糖数据库。我们今年的研究工作有 建立了小型数据库，如CCRC-Net的木葡聚糖 低聚糖数据库，线性相关等技术 系数分析足以确定木聚糖 结构在频谱被理想化之后。然而，对于大的 N-连锁低聚糖数据库--一种非线性分离技术 需要一个相对较小的神经网络，以便 来辨认这个结构。一份手稿正在准备中，描述 这项工作。我们已于#年为这项计划申请单独拨款 美国国立卫生研究院RO1资助金的形式。一项专利申请也正在进行中 为这项工作做好准备。

This project seeks to better understand how neural networks operate in order to automate the presentation of newly collected spectra to the search engine of the corresponding database. We have developed a new methodology for using neural networks that represents a fundamental shift from current methodology. With the new methodology the chemical signature (spectrum) can be presented directly to the neural networks without conducting any pre-processing steps to reduce noise, correct the baseline, or eliminate contaminants and without any loss in classification accuracy. This technology has been successfully applied to the CCRC-Net system to develop search engines for the xyloglucan oligosaccharide and the small N-linked oligosaccharide databases. Our research work this year has established that for small databases, such as CCRC-Net's xyloglucan oligosaccharide database, a linear technique such as correlation coefficient analysis is sufficient to determine the xyloglucan structure after the spectrum has been idealized. However, for large N-linked oligosaccharide databases, a non-linear separation technique is required, such as a relatively small-sized neural network, in order to identify the structure. A manuscript is in preparation describing this work. We have applied for separate funding for this project in the form of an RO1 grant from NIH. A patent application is also being prepared for this work.