Abstract
Biclustering is an unsupervised machine learning technique that simultaneously clusters genes and conditions in gene expression data. Gene Ontology (GO) is usually used in this context to validate the biological relevance of the results. However, although the integration of biological information from different sources is one of the research directions in Bioinformatics, GO is not used in biclustering as an input data. A scatter search-based algorithm that integrates GO information during the biclustering search process is presented in this paper. SimUI is a GO semantic similarity measure that defines a distance between two genes. The algorithm optimizes a fitness function that uses SimUI to integrate the biological information stored in GO. Experimental results analyze the effect of integration of the biological information through this measure. A SimUI fitness function configuration is experimentally studied in a scatter search-based biclustering algorithm.
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Acknowledgements
We would like to thank Spanish Ministry of Science and Innovation, Junta de Andalucía and University Pablo de Olavide for the financial support under projects TIN2011-28956-C02-02, TIN2014-55894-C2-R, P12-TIC-1728 and APPB813097, respectively.
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Nepomuceno, J.A., Troncoso, A., Nepomuceno-Chamorro, I.A., Aguilar–Ruiz, J.S. (2016). Biclustering of Gene Expression Data Based on SimUI Semantic Similarity Measure. In: Martínez-Álvarez, F., Troncoso, A., Quintián, H., Corchado, E. (eds) Hybrid Artificial Intelligent Systems. HAIS 2016. Lecture Notes in Computer Science(), vol 9648. Springer, Cham. https://doi.org/10.1007/978-3-319-32034-2_57
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DOI: https://doi.org/10.1007/978-3-319-32034-2_57
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