Abstract
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 result greatly, thus it is important to evaluate these filter methods in a system way. In this paper, we compare the performance of statistical-based, biological-based 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 showed that with similar or better AUC, precision and less features, filter-based feature selection should be taken into consideration if researchers need fast results when facing complex computing problems in bioinformatics.
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References
Saeys, Y., Inza, I., Larranaga, P.: A review of feature selection techniques in bioinformatics. Bioinformatics 23(19), 2507–2517 (2007)
Rebhan, M., et al.: GeneCards: integrating information about genes, proteins and diseases. Trends Genet. 13(4), 163 (1997)
Becker, K.G., et al.: The genetic association database. Nat. Genet. 36(5), 431–432 (2004)
Ashburner, M., et al.: Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nat. Genet. 25(1), 25–29 (2000)
Nishimura, D.: BioCarta. Biotech Software & Internet Report 2(3), 117–120 (2001)
Kanehisa, M., et al.: Data, information, knowledge and principle: back to metabolism in KEGG. Nucleic Acids Res. 42(1), 199–205 (2014)
Schaefer, C.F., et al.: PID: the Pathway Interaction Database. Nucleic Acids Res. 37(Database issue), D674–D679 (2009)
Croft, D., et al.: The Reactome pathway knowledgebase. Nucleic Acids Res. 42(1), D472–D477 (2014)
Hsu, S.D., et al.: miRTarBase: a database curates experimentally validated microRNA-target interactions. Nucleic Acids Res. 39(Database issue), D163–D169 (2011)
Cai, Y., et al.: Fast Implementation of l1 Regularized Learning Algorithms Using Gradient Descent Methods. In: Proceedings of the 10th SIAM International Conference on Data Mining (SDM 2010), Columbus, Ohio, USA, pp. 862–871 (2010)
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Wang, Y., Fan, X., Cai, Y. (2014). A Comparative Study of Improvements Filter Methods Bring on Feature Selection Using Microarray Data. In: Zhang, Y., Yao, G., He, J., Wang, L., Smalheiser, N.R., Yin, X. (eds) Health Information Science. HIS 2014. Lecture Notes in Computer Science, vol 8423. Springer, Cham. https://doi.org/10.1007/978-3-319-06269-3_7
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DOI: https://doi.org/10.1007/978-3-319-06269-3_7
Publisher Name: Springer, Cham
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