Abstract
The phenotype is the result of a genotype expression in a given environment. Genetic and eventually protein mutations and/or environmental changes may affect the biological homeostasis leading to a pathological status of a normal phenotype. Studying the alterations of the phenotypes on a temporal basis becomes thus relevant and even determinant whether considering the biological re-assortment between the involved organisms and the cyclic nature of the pandemic outbreaks. In this paper, we present a computational solution that analyzes phenotype data in order to capture statistically evident changes emerged over time and track their repeatability. The proposed method adopts a model of analysis based on time-windows and relies on two kinds of patterns, emerging patterns and variability patterns. The first one models the changes in the phenotype detected between time-windows, while the second one models the changes in the phenotype replicated over time-windows. The application to Influenza A virus H1N1 subtype proves the usefulness of our in silico approach.
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Acknowledgements
The authors would like to acknowledge the support of the European Commission through the project MAESTRA - Learning from Massive, Incompletely annotated, and Structured Data (Grant number ICT-2013-612944).
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Loglisci, C., Balech, B., Malerba, D. (2015). Discovering Variability Patterns for Change Detection in Complex Phenotype Data. In: Esposito, F., Pivert, O., Hacid, MS., Rás, Z., Ferilli, S. (eds) Foundations of Intelligent Systems. ISMIS 2015. Lecture Notes in Computer Science(), vol 9384. Springer, Cham. https://doi.org/10.1007/978-3-319-25252-0_2
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DOI: https://doi.org/10.1007/978-3-319-25252-0_2
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