A comparative study of improvements Pre-filter methods bring on feature selection using microarray data

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• 作者：Yingying Wang (1)
  Xiaomao Fan (1)
  Yunpeng Cai (1)
  
  1. Research Center for Biomedical Information ; Shenzhen Institutes of Advanced Technologies ; Chinese Academy of Sciences ; Shenzhen ; China
  
• 关键词：Comparative study ; Feature selection ; Microarray
• 刊名：Health Information Science and Systems
• 出版年：2014
• 出版时间：December 2014
• 年：2014
• 卷：2
• 期：1
• 全文大小：927 KB
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• 刊物主题：Health Informatics; Computational Biology/Bioinformatics; Information Systems and Communication Service; Bioinformatics;
• 出版者：BioMed Central
• ISSN：2047-2501

文摘

Background Feature selection techniques have become an apparent need in biomarker discoveries with the development of microarray. However, the high dimensional nature of microarray made feature selection become time-consuming. To overcome such difficulties, filter data according to the background knowledge before applying feature selection techniques has become a hot topic in microarray analysis. Different methods may affect final results greatly, thus it is important to evaluate these pre-filter methods in a system way. Methods In this paper, we compared the performance of statistical-based, biological-based pre-filter methods and the combination of them on microRNA-mRNA parallel expression profiles using L1 logistic regression as feature selection techniques. Four types of data were built for both microRNA and mRNA expression profiles. Results Results showed that pre-filter methods could reduce the number of features greatly for both mRNA and microRNA expression datasets. The features selected after pre-filter procedures were shown to be significant in biological levels such as biology process and microRNA functions. Analyses of classification performance based on precision showed the pre-filter methods were necessary when the number of raw features was much bigger than that of samples. All the computing time was greatly shortened after pre-filter procedures. Conclusions With similar or better classification improvements, less but biological significant features, pre-filter-based feature selection should be taken into consideration if researchers need fast results when facing complex computing problems in bioinformatics.