Two stages weighted sampling strategy for detecting the relation between gene expression and disease.

IF 0.2 4区 生物学 Q4 MATHEMATICAL & COMPUTATIONAL BIOLOGY
Chih-Chung Yang, Wen-Shin Lin, Chien-Pang Lee, Yungho Leu
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引用次数: 2

Abstract

For microarray data analysis, most of them focus on selecting relevant genes and calculating the classification accuracy by the selected relevant genes. This paper wants to detect the relation between the gene expression levels and the classes of a cancer (or a disease) to assist researchers for initial diagnosis. The proposed method is called a Two Stages Weighted Sampling strategy (TSWS strategy). According to the results, the performance of TSWS strategy is better than other existing methods in terms of the classification accuracy and the number of selected relevant genes. Furthermore, TSWS strategy also can use to understand and detect the relation between the gene expression levels and the classes of a cancer (or a disease).

基因表达与疾病关系的两阶段加权抽样检测策略。
对于微阵列数据分析,大多侧重于选择相关基因,并通过选择的相关基因计算分类精度。本文旨在检测基因表达水平与癌症(或疾病)类别之间的关系,以帮助研究人员进行初步诊断。该方法被称为两阶段加权抽样策略(TSWS策略)。结果表明,TSWS策略在分类精度和选择的相关基因数量方面优于其他现有方法。此外,TSWS策略还可用于了解和检测基因表达水平与癌症(或疾病)类别之间的关系。
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来源期刊
CiteScore
1.00
自引率
0.00%
发文量
0
审稿时长
>12 weeks
期刊介绍: Mining bioinformatics data is an emerging area at the intersection between bioinformatics and data mining. The objective of IJDMB is to facilitate collaboration between data mining researchers and bioinformaticians by presenting cutting edge research topics and methodologies in the area of data mining for bioinformatics. This perspective acknowledges the inter-disciplinary nature of research in data mining and bioinformatics and provides a unified forum for researchers/practitioners/students/policy makers to share the latest research and developments in this fast growing multi-disciplinary research area.
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