An Ensemble Approach for Gene Selection in Gene Expression Data

• Autores: José Antonio Castellanos Garzón, Juan Ramos González, Daniel López Sánchez, Juan Francisco de Paz Santana
• Localización: 11th International Conference on Practical Applications of Computational Biology & Bioinformatics / Florentino Fernández Riverola (ed. lit.) , 2017, ISBN 978-3-319-60815-0, págs. 237-247
• Idioma: inglés
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• Resumen

  • Feature/Gene selection is a major research area in the study of gene expression data, generally dealing with classification tasks of diseases or subtype of diseases and identification of biomarkers related to a type of disease. In such a context, this paper proposes an ensemble approach of gene selection for classification tasks from gene expression datasets. This proposal provides a four-staged approach of gene filtering.

    Each stage performs a different gene filtering task, such as: data processing, noise removing, gene selection ensemble and application of wrapper methods to reach the end result, a small subset of informative genes.

    Our proposal has been assessed on two different datasets of the same disease (Pancreatic ductal adenocarcinoma) for which, good results have been achieved in comparison with other gene selection methods. Hence, the proposed strategy has proven its reliability with respect to other approaches.