Feature Selection and Classification by using Grid Computing based Evolutionary Approach for the Microarray Data

机译：利用基于网格计算的进化方法对微阵列数据进行特征选择和分类

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The cancer classification through gene expression patterns becomes one of the most promising applications of the microarray technology. It is also a significant procedure in bioinformatics. In this study a grid computing based evolutionary mining approach is proposed as discriminant function for gene selection and tumor classification. The proposed approach is based on the grid computing infrastructure for establishing the best attributes set. The discriminant analysis based on vector distant of median method as the evaluation function of genetic algorithm which lays stress on find the ke> attributes set of the data set to establish the best attributes set for constructing a classification response model with highest accuracy. We show experimentally that the proposed approach for several benchmarking cancer microarray data sets can work effectively and efficiently, and the results of the proposed methods are superior to or as well as other existing methods in literatures.

机译：通过基因表达模式进行癌症分类成为微阵列技术最有前途的应用之一。这也是生物信息学中的重要程序。在这项研究中，提出了一种基于网格计算的进化挖掘方法，作为基因选择和肿瘤分类的判别函数。所提出的方法基于用于建立最佳属性集的网格计算基础结构。基于中值向量距离作为遗传算法的评估函数的判别分析着重于寻找数据集的ke>属性集，以建立最佳属性集，以构建具有最高准确性的分类响应模型。我们通过实验表明，针对几种基准癌症微阵列数据集的拟议方法可以有效，高效地工作，并且拟议方法的结果优于或优于文献中的其他现有方法。

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《IEEE international conference on computer science and information technology;ICCSIT 2010》|2010年|p.85-89|共5页
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T.-C.Chen; P.-S.You; Y.-C.Hsieh; Y.-C.Lee;
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中图分类计算技术、计算机技术;计算技术、计算机技术;计算技术、计算机技术;
关键词
microarray; tumor classification; grid computing; genetic algorithm;

机译：微阵列肿瘤分类网格计算;遗传算法;

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专利

1. Using a grid computing-based meta-evolutionary mining approach for the microarray data cancer-categorization [J] . Chiang Tai-Wei, Chen Ta-Cheng Engineering Computations . 2017,第1期

机译：使用基于网格计算的元进化挖掘方法对微阵列数据进行癌症分类
2. A novel feature extraction approach based on ensemble feature selection and modified discriminant independent component analysis for microarray data classification [J] . Mollaee Maryam, Moattar Mohammad Hossein Biocybernetics and biomedical engineering . 2016,第3期

机译：基于集成特征选择和改进的判别独立成分分析的特征提取新方法
3. An evolutionary approach for gene selection and classification of microarray data based on SVM error-bound theories [J] . Debnath R, Kurita T BioSystems . 2010,第1期

机译：基于SVM误差约束理论的基因选择和芯片数据分类的进化方法。
4. Feature Selection and Classification by using Grid Computing based Evolutionary Approach for the Microarray Data [C] . T.-C. Chen, P.-S. You, Y.-C. Hsieh, IEEE International Conference on Computer Science and Information Technology . 2010

机译：基于网格计算的微阵列数据的进化方法来专用选择和分类
5. An empirical study of feature selection in binary classification with DNA microarray data. [D] . Lecocke, Michael Louis. 2005

机译：对具有DNA微阵列数据的二元分类中的特征选择进行实证研究。
6. Feature selection and classification for microarray data analysis: Evolutionary methods for identifying predictive genes [O] . Thanyaluk Jirapech-Umpai, Stuart Aitken 2005

机译：微阵列数据分析的特征选择和分类：鉴定预测基因的进化方法
7. Feature selection and classification for microarray data analysis: Evolutionary methods for identifying predictive genes [O] . Jirapech-Umpai Thanyaluk, Aitken Stuart 2005

机译：微阵列数据分析的特征选择和分类：鉴定预测基因的进化方法

Feature Selection and Classification by using Grid Computing based Evolutionary Approach for the Microarray Data

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