Multiclass classification of microarray data samples with a reduced number of genes

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. Results: A novel bound on the maximum number of genes that can be handled by binary classifiers in binary m...

• Type: Journal
• Year: 2011
• Where: BMCBI
• Authors: Elizabeth Tapia, Leonardo Ornella, Pilar Bulacio, Laura Angelone
• Keywords: Artificial Intelligence, BMCBI 2011, Classification Models, Microarray Data Samples, Multiclass Classification