Microarray data classification is one of the most important emerging clinical applications in the medical community. Machine learning algorithms are most frequently used to complete this task. We selected one of the state-of-the-art kernel-based algorithms, the support vector machine (SVM), to classify microarray data. As a large number of kernels are available, a significant research question ...

Microarray technology allows for the expression levels of thousands of genes in a cell to be measured simultaneously. The technology provides great potential in the fields of biology and medicine as the analysis of data obtained from microarray experiments gives insight into the roles of specific genes and the associated changes across experimental conditions (e.g., aging, mutation, radiation t...

DNA microarrays are now widely used to measure gene expression levels of healthy and cancerous cells. To allow further experiment for drug development to treat cancer, colour intensity from images of microarray spots need to be extracted as accurate as possible. The intensity extraction requires pre-requisite analysis stages including noise removal, and followed by location gridding. However, i...

The DNA microarray is an important technique that allows researchers to analyze many gene expression data in parallel. Although the data can be more significant if they come out of separate experiments, one of the most challenging phases in the microarray context is the integration of separate expression level datasets that have gathered through different techniques. In this paper, we prese...

In response to the high levels of dextran being obtained in raw sugars produced at Jamaican sugar factories, newer methods have been investigated to assess the accuracy and appropriateness for routine analysis of dextran in factory products. Two methods, the Dextran And Sucrose Assay (DASA) developed by Optical Activity® with field work in Jamaica and the Monoclonal Antibody (MCA) test develope...

DNA microarrays have gained wide use in biomedical research by simultaneously monitoring the expression levels of a large number of genes. The successful implementation of DNA microarray technologies requires the development of methods and techniques for the fabrication of microarrays, the selection of probes to represent genes, the quantification of hybridization, and data analysis. In this pa...

Feature selection is an important task for mining useful information from datasets in high dimensions, a typical characteristic of biology domains such as microarray datasets. In this paper, we present an altogether new perspective on feature selection. We pose feature selection as a one class SVM problem of modeling the space in which features can be represented. We show that finding the suppo...

With the advance of gene expression data in the bioinformatics field, the questions which frequently arise, for both computer and medical scientists, are which genes are significantly involved in discriminating cancer classes and which genes are significant with respect to a specific cancer pathology. Numerous computational analysis models have been developed to identify informative genes from ...

Lung Cancer is one of the cancer types with most significant mortality rate, mainly because disease's slow detection. Therefore, early identification this disease crucial. However, primary issue microarray curse dimensionality. This problem related to characteristic data, which has a small sample size yet many attributes. Moreover, could lower accuracy detection systems. Various machines and de...

ONE OF THE UNDERLYING PRINCIPLES of scientific research and publication in peer-reviewed journals is the concept of reproducibility, within studies and between laboratories. Since January 1, 2003, Physiological Genomics and the American Physiological Society have adopted the microarray data standard developed by the Microarray Gene Expression Data Society (MGED), requiring that all authors usin...