Sahin N, Aydin S
OFM4 Biyoloji Egitimi, Egitim Fakultesi, Mugla University, 48170 Kotekli, Mugla, Turkey.
Folia Microbiol (Praha). 2006;51(2):87-91. doi: 10.1007/BF02932161.
A new approach with artificial neural network (ANN) was applied to numerical taxonomy of bacteria using the oxalate as carbon and energy source. For this aim the characters effective in differentiating separate groups were selected from morphological, physiological and biochemical test results. Fourteen aerobic, Gram-negative, oxalate-utilizing isolates and four oxalate-utilizing reference strains (Ralstonia eutropha DSM 428, Methylobacterium extorquens DSM 1337T, Ralstonia oxalatica DSM 1105T, Oxalicibacterium flavum DSM 15506T) were included in the study. ANN program used here was developed in Borland C++ language. Iterations were performed on an IBM compatible PC computer. ANN architecture having feed-forward backpropagation algorithm was used for teaching generalized delta rule. The results show that ANN can have a large potential in solving the taxonomic problems of oxalate-utilizing bacteria.
一种采用人工神经网络（ANN）的新方法被应用于以草酸盐作为碳源和能源的细菌数值分类学研究。为此，从形态学、生理学和生化测试结果中选取了对区分不同菌组有效的特征。本研究纳入了14株需氧、革兰氏阴性、利用草酸盐的分离菌株以及4株利用草酸盐的参考菌株（真养产碱菌DSM 428、扭脱甲基杆菌DSM 1337T、草酸雷尔氏菌DSM 1105T、黄草酸杆菌DSM 15506T）。此处使用的ANN程序是用Borland C++语言开发的。在一台IBM兼容个人电脑上进行迭代。采用具有前馈反向传播算法的ANN架构来教授广义delta规则。结果表明，ANN在解决利用草酸盐细菌的分类学问题方面具有很大潜力。
