Abstract
Understanding the key role that miRNAs play in the regulation of gene expression is one of the most important challenges in modern molecular biology. Standard gene set enrichment analysis (GSEA) is not appropriate in this context, due to the low specificity of the relation between miRNAs and their target genes. We developed alternative strategies to gain better insights in the differences in biological processes involved in different experimental conditions. We here describe a novel method to analyze and interpret miRNA expression data correctly, and demonstrate that annotating miRNA directly to biological processes through their target genes (which is nevertheless the only way possible) is a non-trivial task. We are currently employing the same strategy to relate miRNA expression patterns directly to pathway information, to generate new hypotheses, which may be relevant for the interpretation of their role in the gene expression regulatory processes.
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Nuzzo, A. et al. (2011). A Data Mining Library for miRNA Annotation and Analysis. In: Peleg, M., Lavrač, N., Combi, C. (eds) Artificial Intelligence in Medicine. AIME 2011. Lecture Notes in Computer Science(), vol 6747. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-22218-4_10
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DOI: https://doi.org/10.1007/978-3-642-22218-4_10
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