NEURAL NETWORK 2D ELECTROPHORESIS PROTEIN IDENTIFICATION

电泳可以解析数千种蛋白质，但对此类蛋白质的分析 数据极其困难。 自动化检测方法 二维 (2D) 电泳图上蛋白质的识别 需要。 图像处理技术和专家系统已经尝试过， 但这些都是计算密集型的，需要启发式规则库， 并且无法通过分析蛋白质的成分来识别蛋白质 多肽链。 人工神经网络易于训练和 实施，并且应该正确分类几乎无限数量的 蛋白质通过其独特的多肽模式。 开发一个 基于神经网络识别的蛋白质识别系统 提出了二维电泳图中的多肽分布。 阶段 我研究将开发一个演示系统来检查 未知蛋白质的电泳图并对其进行鉴定。 努力 是 ORINCON 在此类领域成功的神经网络研究的延伸 生物医学诊断、水下声学和故障等不同领域 检测。 第二阶段将结合数据融合技术和 扩展了一组已知蛋白质以实现整个二维分析 电泳生物样品。 这将导致发展 用于二维自动化蛋白质分析的商业软件包 电泳图。 可以轻松修改它以对其他内容进行分类 只需重新训练即可识别成分（例如氨基酸和核酸） 净。

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.