Vohradský J
Czech Academy of Sciences, Institute of Microbiology, Prague, Czech Republic.
Electrophoresis. 1997 Dec;18(15):2749-54. doi: 10.1002/elps.1150181508.
The interpretation of two-dimensional gel electrophoresis spot profiles can be facilitated by statistical and machine learning programs. Two different approaches to classification of spot profiles - cluster analysis and neural networks - are discussed. Neural networks for two different model patterns were designed and an algorithm for training of the net for the classification was developed. It was shown that the performance of neural networks is higher compared to cluster and principal component analysis. The possibility of combining both approaches into one process can increase reliability and speed of classification. Artificially created training sets with added random noise can be used for network training. The analysis was applied on the Streptomyces coelicolor developmental two-dimensional (2-D) gel database.
统计和机器学习程序有助于二维凝胶电泳斑点图谱的解读。本文讨论了两种不同的斑点图谱分类方法——聚类分析和神经网络。设计了针对两种不同模型模式的神经网络，并开发了一种用于网络训练以进行分类的算法。结果表明，与聚类分析和主成分分析相比，神经网络的性能更高。将这两种方法结合到一个过程中的可能性可以提高分类的可靠性和速度。带有附加随机噪声的人工创建训练集可用于网络训练。该分析应用于天蓝色链霉菌发育二维凝胶数据库。
