Non-linear Analysis of Medical and Pharmaceutical Data using Neural Network and Generalized Additive Model

使用神经网络和广义加性模型对医疗和制药数据进行非线性分析

• 批准号: 11672140
• 负责人: TAKAGI Tatsuya
• 金额: $ 0.83万
• 依托单位: Osaka University
• 依托单位国家: 日本
• 项目类别: Grant-in-Aid for Scientific Research (C)
• 财政年份: 1999
• 资助国家: 日本
• 起止时间: 1999 至 2000
• 项目状态: 已结题
• 来源: https://kaken.nii.ac.jp/grant/KAKENHI-PROJECT-11672140/
• 关键词: Nonlinear Analysis; Nonparametric Regression; Decision Tree; Artificial Neural Network; Metric Pharmacy; Pharmainformatics; Pharmacoepidemiology; 平滑化法

We have tried to adopt the several nonparametric data analysis methods, such as GAM, MARS, decision tree, MART, hierarchical artificial neural networks (HANN), and Livingstone type artificial neural networks (LANN), to the data in the field of medical and pharmaceutical sciences. And the reasonable results of regression and principal component analyses were obtained.First, we compared the two nonparametric nonlinear regression methods, MARS and MART, which were developed by Freedman et al., using ideal artificial data. MARS showed a surprisingly good fitting and prediction performances ; MART also showed quite good performances. And the results indicate that MART is a robust data mining method. Thus, we resulted that the MARS should be adopted for the data containing little noise and that the MART should be adopted for the data analyses which can be affected by noise.Then, we applied these methods including the GAM and the HANN to some epidemiological data sets. For example, the MARS a … More nd the MART methods were applied to the pharmacoepidemiological study of estrogen, which might be related to the onset of endometrial cancer. The results show that the active form of estrogen is related to the onset of the cancer. Although we could get a certain information of the pharmacoepidemiological study of estrogen, it was not so easy to test the significance of each prediction variables used in such nonparametric models. Thus, we introduced the extended shift test method, which was revised by our group, in order to test the significance of prediction variables. These methods were also applied to the clinical epidemiological study on the relation between onset of esophagus cancer and alcohol consumption. We confirmed the possibility that the alcohol consumption affect the esophagus cancer.In addition, we applied the LANN to the nonlinear principal component analyses of Gas-Liquid chromatographic retention data. The clearer classified result than the one obtained by linear PCA method could be obtained. Less

我们试图采用几种非参数数据分析方法，例如GAM，MARS，决策树，MART，分层人工神经元网络（HANN）和Livingstone型人工神经元网络（LANN），用于医学和药学科学领域的数据。并获得了回归和主成分分析的合理结果。首先，我们比较了两种非参数非线性回归方法，火星和火星，MARS表现出令人惊讶的良好拟合和预测性能。玛特还表现出色。结果表明MART是一种强大的数据挖掘方法。这就是我们导致的火星应用于几乎没有噪声的数据，并且应采用MART来用于可能受噪声影响的数据分析。然后，我们将这些方法（包括GAM和HANN）应用于一些流行病学数据集。例如，火星a…更多地将MART方法应用于雌激素的药物ePIDEMIological研究，这可能与子宫内膜癌的发作有关。尽管我们可以获取雌激素药物ePIDEMIologicy研究的某些信息，但是测试此类非参数模型中使用的每个预测变量的重要性并不容易。这是我们介绍的扩展移位测试方法，该方法是由我们小组修订的，以测试预测变量的重要性。这些方法还应用于有关食道癌和饮酒发作之间关系的临床流行病学研究。我们证实了酒精消耗会影响食道癌的可能性。此外，我们将LANN应用于气液色谱保留数据的非线性主要成分分析。可以获得比线性PCA方法获得的更清晰的分类结果。较少的

项目成果

Kurokawa K.,Takagi T.,Yasunaga T.: "The Approach for Bacterial Phenotype Representation by using Bacterial Whole Genomes"Proceedings of International Conference of Science of Systematic Biology. 1. 173-178 (2000)

Kurokawa K.，Takagi T.，Yasunaga T.：“使用细菌全基因组表示细菌表型的方法”国际系统生物学科学会议论文集。

A.V.Afonin et al.: "Specific intermolecular interactions C-H-N in heteroaryl vinyl ethers and hetero aryl rinyl sulfides studied by 'H, C^<13>, and N^<15> NMR sectroscopies…・"Can. J. Chem.. 77. 416-424 (1999)

A.V.Afonin 等人：“通过 H、C^<13> 和 N^<15> NMR 显微镜研究杂芳基乙烯基醚和杂芳基环硫醚中的特定分子间相互作用 C-H-N……”Can. J. Chem.. 77 . 416-424 (1999)

Kurokawa K., Takagi T., Yasunaga T.: "The Approach for Bacterial Phenotype Representation by using Bacterial Whole Genomes."Proceedings of International Conference of Science of Systematic Biology. vol.1. 173-178 (2000)

Kurokawa K.、Takagi T.、Yasunaga T.：“使用细菌全基因组表示细菌表型的方法”。国际系统生物学科学会议论文集。