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Classical methods for the detection of gene expression differences between two microarray conditions often fail to detect interesting and important differences, because they are weak in comparison with the overall variability. Therefore, methodologies that highlight weak differences are needed. Here, we propose a method that allows the fusion of other genomic data with microarray data and show, through...
Within a gene expression matrix, there are usually several particular macroscopic phenotypes of samples related to some diseases or drug effects, such as diseased samples, normal samples or drug treated samples. The goal of sample-based clustering is to find the phenotype structures of these samples. A novel method for automatically discovering clusters of samples which are coherent from a genetic...
The application of information technology in the field of biomedicine has become increasingly important over the last several years. The different possibilities for the workflow in the microarray analysis can be huge and it would be very interesting to create an automatic process for establishing the workflows. This paper presents an intelligent dynamic architecture based on intelligent organizations...
In this work we study the utilization of several ensemble alternatives for the task of classifying microarray data by using prior knowledge known to be biologically relevant to the target disease. The purpose of the work is to obtain an accurate ensemble classification model able to outperform baseline classifiers by introducing diversity in the form of different gene sets. The proposed model takes...
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