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In this paper we present a new approach for classification of microarray data. Our methodology consists of two steps: an attribute selection, which aims at selection of the most informative genes, and a classification of expression profiles, which is carried out by weighted voting, a novel instance-based classifier based on Rough Set Theory. Attribute selection consists of two stages - initial selection,...
This paper deals with the advanced and developed methodology know for cancer multi classification using an Extreme Learning Machine (ELM) for microarray gene expression cancer diagnosis, this used for directing multicategory classification problems in the cancer diagnosis area. ELM avoids problems like local minima; improper learning rate and over fitting commonly faced by iterative learning methods...
Selecting differentially expressed genes (DEGs) is one of the most important tasks in microarray applications. However, the sample sizes typically used in current cancer studies may only partially reflect the widely altered gene expressions in cancers. By analyzing three large cancer datasets, we show that, in each cancer, a wide range of functional modules are altered and have high disease classification...
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