Article detail

Authors: Tapia, Elizabeth; Ornella, Leonardo; Bulacio, Pilar; Angelone, Laura.


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  mediated multiclass classification algorithms of microarray data samples is presented. The bound suggests that  high-dimensional binary output domains might favor the existence of accurate and sparse binary mediated multiclass classifiers for microarray data samples.

Conclusions: A comprehensive experimental work shows that the bound is indeed useful to induce accurate and  sparse multiclass classifiers for microarray data samples.



articulo_idioma: Inglés.


Editing place: Londres.

Publishing country: Reino Unido.

Reference type: Con Referato.

ISSN: 1471-2105.