NEURAL NET FOR 2D ELECTROPHORESIS PROTEIN IDENTIFICATION

用于二维电泳蛋白质鉴定的神经网络

• 批准号: 2545355
• 负责人: THOMAS W BROTHERTON
• 金额: $ 22.56万
• 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 1999-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/2545355
• 关键词: artificial intelligence; bioengineering /biomedical engineering; computational neuroscience; electrophoresis; image processing; method development; 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, 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.

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，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已经开发出一种蛋白质 一种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 蛋白质在二维电泳图中的分布。该系统演示了 区分和正确分类不同类型的可行性 临床条件下的二维电泳图。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斑点 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。这项技术将在一台 斯克里普斯诊所和大规模生物学(LSB)收集的大型数据集 公司。这将导致开发一个商业包，用于 2D电泳图的自动蛋白质分析。这很容易就是 对其他成分(例如，氨基酸和核酸)进行分类的 通过分析适当的数据集。 建议的商业应用：研究应引导开发 用于2D电泳自动处理的商业软件包的开发 蛋白质数据。这项技术的应用将会广泛；在 除了电泳法普遍存在的实验室外， 技术可以应用到临床环境中，在这种环境中，患者蛋白质 分析将是区分的一个非常重要的诊断工具 在正常状态和疾病状态之间。这项技术可能会被使用 作为癌症筛查工具。100多万例癌症被诊断出来 在美国每年都有。