Biclustering-Based Classification of Clinical Expression Time Series: A Case Study in Patients with Multiple Sclerosis
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.
KeywordsPrediction Accuracy Good Responder Score Matrix Expression Time Series Gene Expression Time Series
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