Abstract
In the last years the constant drive towards a more personalized medicine led to an increasing interest in temporal gene expression analyses. In fact, considering a temporal aspect represents a great advantage to better understand disease progression and treatment results at a molecular level. In this work, we analyse multiple gene expression time series in order to classify the response of Multiple Sclerosis patients to the standard treatment with Interferon-β , to which nearly half of the patients reveal a negative response. In this context, obtaining a highly predictive model of a patient’s response would definitely improve his quality of life, avoiding useless and possibly harmful therapies for the non-responder group. We propose new strategies for time series classification based on biclustering. Preliminary results achieved a prediction accuracy of 94.23% and reveal potentialities to be further explored in classification problems involving other (clinical) time series.
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Carreiro, A.V., Anunciação, O., Carriço, J.A., Madeira, S.C. (2011). Biclustering-Based Classification of Clinical Expression Time Series: A Case Study in Patients with Multiple Sclerosis. In: Rocha, M.P., Rodríguez, J.M.C., Fdez-Riverola, F., Valencia, A. (eds) 5th International Conference on Practical Applications of Computational Biology & Bioinformatics (PACBB 2011). Advances in Intelligent and Soft Computing, vol 93. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-19914-1_31
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DOI: https://doi.org/10.1007/978-3-642-19914-1_31
Publisher Name: Springer, Berlin, Heidelberg
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