NEURAL NETWORK 2D ELECTROPHORESIS PROTEIN IDENTIFICATION

神经网络二维电泳蛋白质鉴定

• 批准号: 3493562
• 负责人: THOMAS W BROTHERTON
• 金额: $ 5万
• 依托单位: ORINCON CORPORATION
• 依托单位国家: 美国
• 财政年份: 1993
• 资助国家: 美国
• 起止时间: 1993-09-01 至 1994-02-28
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/3493562
• 关键词: analytical method; artificial intelligence; biomedical automation; computer data analysis; computer system design /evaluation; electrophoresis; peptides; proteins

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.

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，需要启发式规则库， 无法通过分析蛋白质的成分 多肽链。 人工神经网络易于训练， 实施，并应正确分类几乎无限数量的 蛋白质通过其独特的多肽模式。 发展 基于神经网络识别的蛋白质鉴定系统 提出了多肽在二维电泳图谱中的分布。 相 我研究将开发一个演示系统来检验 一种未知蛋白质的蛋白质图谱，并确定它。 是ORINCON成功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 侦测 第二阶段将采用数据融合技术， 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。 这将导致发展 一个商业软件包，用于2D蛋白质自动分析， 电子照相。 它很容易被修改，以分类其他 成分（例如，氨基酸和核酸）， net.