NEURAL NETWORK 2D ELECTROPHORESIS PROTEIN IDENTIFICATION

电泳法可以分解成千上万的蛋白质，但对这些蛋白质的分析 数据是极其困难的。自动检测和检测的方法 蛋白质在二维(2D)电泳图上的识别 需要的。已经尝试了图像处理技术和专家系统， 但这些都是计算密集型的，需要启发式规则基础， 并且不能通过分析它们的组成来识别蛋白质 多肽链。人工神经网络很容易训练和使用 实现，并且应该正确地将几乎无限数量的 蛋白质通过其独特的多肽模式。开发一种 基于神经网络识别的蛋白质识别系统 提出了多肽在二维电泳图中的分布规律。阶段 我会研究开发一个演示系统来检验 一种未知蛋白质的电泳图并进行鉴定。付出的努力 是ORINCON在这方面成功的神经网络研究的延伸 生物医学诊断、水下声学和故障等多个领域 侦测。第二阶段将融合数据融合技术和 扩展了一组已知蛋白质，从而能够分析整个2D 生物样品的电泳法。这将导致发展 用于二维蛋白质自动分析的商用软件包 电泳图。可以很容易地对其进行修改，以将其他 成分(例如，氨基酸和核酸)只需通过重新训练 NET。

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, require heuristic rule bases, and cannot identify proteins from analysis of their constituent polypeptide chains. Artificial neural networks are easy to train and implement, and should correctly categorize an almost unlimited number of proteins by their distinctive polypeptide patterns. Development of a protein identification system based on neural net recognition of polypeptide distribution in 2D electrophoretograms is proposed. Phase I research will develop a demonstration system to examine electrophoretograms of an unknown protein and identify it. The effort is an extension of successful neural net research by ORINCON in such diverse fields as biomedical diagnostics, underwater acoustics, and fault detection. Phase II will incorporate data fusion techniques and an expanded set of known proteins to enable analysis of entire 2D electrophoresis biological samples. This would lead to development of a commercial package for automated protein analysis of 2D electrophoretograms. It easily could be modified to categorize other constituents (e.g., amino and nucleic acids) simply by retraining the net.