Multiclass classification of microarray data samples with a reduced number of genes |
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Authors: | Elizabeth Tapia Leonardo Ornella Pilar Bulacio Laura Angelone |
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Institution: | 1.CIFASIS-Conicet Institute,Rosario,Argentina;2.Facultad de Cs. Exactas e Ingeniería,National University of Rosario,Argentina |
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Abstract: | Background Multiclass classification of microarray data samples with a reduced number of genes is a rich and challenging problem in Bioinformatics
research. The problem gets harder as the number of classes is increased. In addition, the performance of most classifiers
is tightly linked to the effectiveness of mandatory gene selection methods. Critical to gene selection is the availability
of estimates about the maximum number of genes that can be handled by any classification algorithm. Lack of such estimates
may lead to either computationally demanding explorations of a search space with thousands of dimensions or classification
models based on gene sets of unrestricted size. In the former case, unbiased but possibly overfitted classification models
may arise. In the latter case, biased classification models unable to support statistically significant findings may be obtained. |
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