Abstract
Gene expression microarray technology provides the global information on transcriptional activities of essentially all genes simultaneously, and it thus promotes the new application of traditional feature selection methods in the fields of molecular biology and life sciences. The basic strategy for the traditional feature selection methods is to seek for a single gene subset that leads to the best prediction of biological types, for example tumor versus normal tissues. Because of complexities and genetic heterogeneities of biological phenotypes (e.g. complex diseases), robust computational approaches are desirable to achieve high generalization performance with multiple classifiers and perturbations of the data structures. The purpose of this study is to develop an ensemble decision approach to analysis of multiple heterogeneous phenotypes. The results from an application to a lymphoma data of five subtypes indicate that the proposed analysis strategy is feasible and powerful to perform biological subtype.
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Wang, H., Zhang, Q., Wang, Y., Li, X., Rao, S., Ding, Z. (2005). A Novel Feature Ensemble Technology to Improve Prediction Performance of Multiple Heterogeneous Phenotypes Based on Microarray Data. In: Wang, L., Jin, Y. (eds) Fuzzy Systems and Knowledge Discovery. FSKD 2005. Lecture Notes in Computer Science(), vol 3614. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11540007_109
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DOI: https://doi.org/10.1007/11540007_109
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28331-7
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