Abstract
Commonly, in gene expression microarray measurements multiple missing expression values are generated, and the proper handling of missing values is a critical task. To address the issue, in this paper a novel methodology, based on compressive sensing mechanism, is proposed in order to analyze gene expression data on the basis of topological characteristics of gene expression time series. The approach conceives, when data are recovered, their processing through a non-linear PCA for dimensional reduction and a Hierarchical Clustering Algorithm for agglomeration and visualization. Experiments have been performed on the yeast Saccharomyces cerevisiae dataset by considering different percentages of information loss. The approach highlights robust performance when high percentage of loss of information occurs and when few sampling data are available.
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Acknowledgments
The research was developed when Davide Nardone was a M.Sc. student in Applied Computer Science at University of Naples Parthenope.
This work was partially funded by the University of Naples Parthenope (Sostegno alla ricerca individuale per il triennio 2016–2018 project, and supported by Gruppo Nazionale per il Calcolo Scientifico (GNCS-INdAM)).
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Ciaramella, A., Nardone, D., Staiano, A. (2020). Compressive Sensing and Hierarchical Clustering for Microarray Data with Missing Values. In: Raposo, M., Ribeiro, P., Sério, S., Staiano, A., Ciaramella, A. (eds) Computational Intelligence Methods for Bioinformatics and Biostatistics. CIBB 2018. Lecture Notes in Computer Science(), vol 11925. Springer, Cham. https://doi.org/10.1007/978-3-030-34585-3_1
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DOI: https://doi.org/10.1007/978-3-030-34585-3_1
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