NEURAL NET FOR 2D ELECTROPHORESIS PROTEIN IDENTIFICATION

电泳可以解析数千种蛋白质，但对这些蛋白质的分析， 数据非常困难。自动化检测方法， 蛋白质在二维（2D）层析图上的识别是 needed.已经尝试了图像处理技术和专家系统， 但是这些是计算密集型的并且需要启发式规则库。 在第一阶段和最近的IR&D项目中，ORINCON开发了一种蛋白质， 基于自动图像点对应的识别系统 处理和使用神经网络的层次结构来识别 蛋白质分布在2D荧光图中。该系统展示了 区分和正确分类的可行性 2D脑血管造影中的临床状况。在第二阶段，我们将进一步 开发这些技术来处理大量的图像点， 一组扩展的已知蛋白质，使整个2D分析成为可能 电泳生物样品。这项技术将在一个 由Scripps Clinic和Large Scale Biology（LSB）收集的大型数据集 Corporation.这将导致开发一个商业软件包， 2D蛋白质反射图的自动化蛋白质分析。它可以很容易地 修改以分类其它成分（例如，氨基酸和核酸） 通过分析适当的数据集。 建议的商业应用：研究应导致发展 用于2D电泳的自动化处理的商业包装 蛋白质数据这项技术的应用将是广泛的;在 除了电泳很常见的实验室外， 技术可以应用于临床环境，其中患者蛋白质 分析将是一个非常重要的诊断工具， 正常和患病之间的区别这项技术可以用于 作为癌症筛查工具。超过100万例癌症被诊断出来， 在美国每年。

Electrophoresis can resolve thousands of proteins, but analysis of such data is extremely difficult. Automated methods for detection and recognition of proteins on two-dimensional (2D) electrophoretograms are needed. Image processing techniques and expert systems have been tried, but these are computationally intensive, and require heuristic rule bases. In Phase I and on a recent IR&D project, ORINCON has developed a protein identification system based on automated image spot correspondence processing and the use of a hierarchy of neural nets for recognition of protein distribution in 2D electrophoretograms. This system demonstrates the feasibility of differentiating and correctly classifying different clinical conditions in 2D electrophoretograms. In Phase II we will further develop these techniques to handle large numbers-of image spots for an expanded set of known proteins to enable analysis of entire 2D electrophoresis biological samples. The technique will be tested on a large data set collected by Scripps Clinic and Large Scale Biology (LSB) Corporation. This would lead to development of a commercial package for automated protein analysis of 2D electrophoretograms. It could easily be modified to categorize other constituents (e.g., amino and nucleic acids) via analysis of appropriate data sets. PROPOSED COMMERCIAL APPLICATION: The research should lead to development of a commercial package for automated processing of 2D electrophoresis protein data. Applications of this technology would be widespread; in addition to laboratories, in which electrophoresis is common, the technology could be applied to clinical settings, in which patient protein analysis would be a very important diagnostic tool for differentiation between normal and diseased states. This technique can potentially be used as a cancer screening tool. Over one million cases of cancer are diagnosed in the U.S. every year.