Microarrays first appeared on the scene around 1995, and it was not long before their use became quite widespread. Early analyses were modelled on those of the pioneers. By around 2000, however, ...

In microarray data there are a number of biological samples, each assessed for the level of gene expression for a typically large number of genes. There is a need to examine these data with ...

DNA microarrays represent an important new method for determining the complete expression profile of a cell. In “spotted” microarrays, slides carrying spots of target DNA are hybridized to ...

Computational analysis methods including machine learning have a significant impact in the fields of genomics and medicine. High-throughput gene expression analysis methods such as microarray ...

This project explores a GEO (Gene Expression Omnibus) microarray dataset using Python. It demonstrates skills in data handling, visualization, biological interpretation, and working with ...

Muscle tissue is involved with every stage of life activities and has roles in biological processes. For example, the blood circulation system needs the heart muscle to transport blood to all parts, ...

Microarray gene expression data are analyzed by means of a Bayesian nonparametric model, with emphasis on prediction of future observables, yielding a method for selection of differentially expressed ...

The power and promise of microarrays are vast. Offering the ability to run tens of thousands of experiments in parallel on a small glass slide, gene chips have transformed functional genomics, whether ...

Abstract: Data mining is a process which discovers patterns and retrieval knowledge in large datasets. Many learning and data mining algorithms rely on distance metrics. Cluster analysis is one of ...

Abstract: The advance of high throughput biotechnology enables the generation of large amount of biomedical data. The microarray is increasingly a popular approach for the detection of genome-wide ...