Abstract
Gene interaction modeling is a fundamental step in the understanding of cellular functions. The high throughput technologies (microarrays, …) generate a large volume of gene expression data. However, gene expression data mining is a very complex process, it becomes necessary to analyze these data to discover new knowledge about genes and their interactions in purpose to model the Gene Regulatory Network GRN.
In this paper, we compare some patterns extraction approaches used in the literature to infer Gene Regulatory Networks and we propose to use gradual patterns of the form (when A increases, B decreases) to extract knowledge about genes. Furthermore, we rely on GO Gene Ontology as a knowledge source to semantically annotate genes and to add information that can be useful in the process of knowledge extraction.
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Gouider, M., Hamdi, I., Ben Ghezala, H. (2018). Mining Gene Expression Data: Patterns Extraction for Gene Regulatory Networks. In: Abraham, A., Muhuri, P., Muda, A., Gandhi, N. (eds) Intelligent Systems Design and Applications. ISDA 2017. Advances in Intelligent Systems and Computing, vol 736. Springer, Cham. https://doi.org/10.1007/978-3-319-76348-4_8
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DOI: https://doi.org/10.1007/978-3-319-76348-4_8
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