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

我们尝试采用GAM、MARS、决策树、MART、分层人工神经网络(HANN)、Livingstone类型人工神经网络(LANN)等几种非参数数据分析方法对医学和制药领域的数据进行分析。首先，利用理想的人工数据，对Freedman等人提出的两种非参数非线性回归方法MARS和MART进行了比较。玛氏的拟合和预测表现出人意料地好；马特也表现得相当好。结果表明，MART是一种稳健的数据挖掘方法。因此，对于噪声含量较小的数据应采用MARS方法，对可能受噪声影响的数据分析应采用MART方法，然后将GAM和HANN等方法应用于一些流行病学数据集。例如，火星是一种…更多地将MART方法应用于雌激素的药物流行病学研究，认为雌激素可能与子宫内膜癌的发生有关。结果表明，雌激素的活性形式与癌症的发生有关。虽然我们可以获得雌激素的药物流行病学研究的一定信息，但要检验在这种非参数模型中使用的每个预测变量的显著性并不容易。因此，我们引入了本课题组修订的扩展移位检验法，以检验预测变量的显著性。这些方法也被应用于食道癌发病与饮酒关系的临床流行病学研究。证实了饮酒影响食道癌的可能性，并将人工神经网络应用于气-液色谱保留数据的非线性主成分分析。与线性主成分分析方法相比，可以得到更清晰的分类结果。较少

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。