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Weighted-SAMGSR: combining significance analysis of microarray-gene set reduction algorithm with pathway topology-based weights to select relevant genes

机译：加权SAMGSR：将微阵列基因集约简算法的显着性分析与基于路径拓扑的权重相结合以选择相关基因

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摘要

BackgroundIt has been demonstrated that a pathway-based feature selection method that incorporates biological information within pathways during the process of feature selection usually outperforms a gene-based feature selection algorithm in terms of predictive accuracy and stability. Significance analysis of microarray-gene set reduction algorithm (SAMGSR), an extension to a gene set analysis method with further reduction of the selected pathways to their respective core subsets, can be regarded as a pathway-based feature selection method.

机译：背景技术已经证明，在预测准确性和稳定性方面，在特征选择过程中将生物学信息整合到途径中的基于途径的特征选择方法通常优于基于基因的特征选择算法。微阵列基因集减少算法（SAMGSR）的意义分析是基因集分析方法的扩展，可以进一步减少所选途径至其各自核心子集的途径，可以视为基于途径的特征选择方法。

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期刊名称 Biology Direct
作者
Suyan Tian; Howard H. Chang; Chi Wang;
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作者单位

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年(卷),期 2016(11),-1
年度 2016
页码 50
总页数 15
原文格式 PDF
正文语种
中图分类生物学;
关键词
Pathway knowledge Pathway-based feature selection Significance analysis of microarray (SAM) Weighted gene expression profiles Non-small cell lung cancer (NSCLC) Multiple sclerosis (MS);

机译：途径知识;基于途径的特征选择;微阵列的意义分析（SAM）;加权基因表达谱;非小细胞肺癌（NSCLC）;多发性硬化症（MS）;

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专利

1. Weighted-SAMGSR: combining significance analysis of microarray-gene set reduction algorithm with pathway topology-based weights to select relevant genes [J] . Suyan Tian, Howard H. Chang, Chi Wang Biology Direct . 2016,第1期

机译：加权SAMGSR：将微阵列基因集约简算法的显着性分析与基于路径拓扑的权重相结合，以选择相关基因
2. Classification of Non-Small Cell Lung Cancer Using Significance Analysis of Microarray-Gene Set Reduction Algorithm [J] . Lei Zhang, Linlin Wang, Bochuan Du, BioMed research international . 2016,第10期

机译：使用微阵列 - 基因集减量算法的显着分析非小细胞肺癌的分类
3. Sparse linear discriminant analysis for simultaneous testing for the significance of a gene set/pathway and gene selection [J] . Wu MC, Zhang L, Wang Z, Bioinformatics . 2009,第9期

机译：稀疏线性判别分析，可同时测试基因集/途径和基因选择的重要性
4. Outlier Gene Set Analysis Combined with Top Scoring Pair Provides Robust Biomarkers of Pathway Activity [C] . Michael F. Ochs, Jason E. Farrar, Michael Considine, IAPR international conference on pattern recognition in bioinformatics . 2013

机译：离群基因集分析与最高评分对相结合，提供了通路活动的可靠生物标记
5. Selecting neural network topologies: A hybrid approach combining genetic algorithms and neural networks. [D] . Taylor, Christopher M. 2005

机译：选择神经网络拓扑：结合遗传算法和神经网络的混合方法。
6. Classification of Non-Small Cell Lung Cancer Using Significance Analysis of Microarray-Gene Set Reduction Algorithm [O] . Lei Zhang, Linlin Wang, Bochuan Du, 2006

机译：利用微阵列基因集约简算法的意义分析对非小细胞肺癌的分类
7. Weighted-SAMGSR: combining significance analysis of microarray-gene set reduction algorithm with pathway topology-based weights to select relevant genes [O] . 2016

机译：加权SAMGSR：将微阵列基因集约简算法的显着性分析与基于路径拓扑的权重相结合，以选择相关基因

Weighted-SAMGSR: combining significance analysis of microarray-gene set reduction algorithm with pathway topology-based weights to select relevant genes

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