径向基函数网络用于细菌的MALDI-TOF-MS分类  

Radial Basis Function Networks Applied in Bacterial MALDI-TOF-MS Classification

作　　者：张卓勇[1] 王丹[1] Peter de B.Harrington Kent J.Voorhees Jon Rees 

机构地区：[1]东北师范大学化学学院 [2]Department of Chemistry and Biochemistry,Ohio University,Athens,Ohio 45701-2979 [3]Department ofChemistry and Geochemistry,Colorado School ofMines,Golden,CO 80401

出　　处：《计算机与应用化学》2002年第1期45-48,共4页Computers and Applied Chemistry

基　　金：教育部中青年骨干教师基金

摘　　要：径向基函数(RBF)网络被用于根据基质辅助激光解吸/电离飞行时间质谱(MALDI-TOF-MS)对细菌的分类辨识。为了加速网络训练和减少网络的复杂性,本文采用小波变换对原始质谱数据进行压缩,将原来的13828个数据点压缩至328个,且保持了原来的特征谱峰。本文研究了在不同培养时间(24、48和72小时)的5种细菌分类,并对RBF网络参数的影响做了详细地研究,为生物学研究提供了有用的信息。结果表明,约60％以上的细菌样本能够被正确地分类辨识。由于细菌培养的生物学影响因素复杂,因而进一步严格控制细菌的培养条件是改善细菌分类正确率的关键。Radial basis function (RBF) networks were applied to differentiate bacteria cultured at different times (24, 48, and 72 hours, respectively) based on matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF-MS) . To speed up the network training and reduce the number of variables of the original MS data, wavelet transformation was used to compress the original data from 13826 to 328. The feature peaks of the MALDI mass spectra were maintained in the compressed data. The classification of different species of bacteria cultured at different times was studied and the effect of network parameters was investigated, which may provide more information for biological studies. Results showed that about 60% bacteria samples could be i-dentified correctly. Owing to the complexity of biological effects in bacterial growth, more rigid control of bacterial culture conditions seems a critical factor for improving the rate of correctness for bacterial classifications.

关 键 词：径向基函数网络 细菌 分类 基质辅助激光解解吸/电离 飞行时间质谱 

分 类 号：Q939.1[生物学—微生物学] TP18[自动化与计算机技术—控制理论与控制工程]