Multiple myeloma gene expression data was analyzed and Self Organization Prediction Model (SOPM) based on Self-Organization Mapping (SOM) networks was established for predicting multiple myeloma.
本研究基于自组织映射网络(SOM)，分析多骨髓瘤基因表达数据，建立预测多骨髓瘤的自组织预测模型(SOPM)。
The gene linear profile model, composed of model profiles and coefficients, is obtained by ica from gene expression data, so gene classification based on ica is presented.
利用ICA对基因微阵列表达谱数据进行分解获得由基因模型谱和对应系数构成的线性谱模型，并在此基础上进行基因分类。
One model is fuzzy cluster analysis of gene expression data based on a cluster validity measure named Xie-Beni index.
一种模型是基于有效性测度谢白尼指数的基因表达数据的模糊聚类分析。
To integrate the results of clustering data mining and based on visual model requirement, network structure diagram of HQDS would be drawn, gene network structure be study with RT-PCR.
结合挖掘结果，运用网络结构可视化思想构建心气虚证网络结构图，并运用逆转录聚合酶链反应(RT - PCR)探索心气虚证基因网络结构。
To integrate the results of clustering data mining and based on visual model requirement, network structure diagram of HQDS would be drawn, gene network structure be study with RT-PCR.
结合挖掘结果，运用网络结构可视化思想构建心气虚证网络结构图，并运用逆转录聚合酶链反应(RT - PCR)探索心气虚证基因网络结构。
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