A DNA microarray is a multiplex technology used in molecular biology and in medicine. It consists of an arrayed series of thousands of microscopic spots of DNA oligonucleotides, called features, each containing picomoles of a specific DNA sequence. Microarray data was found to be more useful when compared to other similar datasets. The sheer volume (in bytes), specialized formats (such as MIAME), and curation efforts associated with the datasets require specialized databases to store the data.
There is little doubt about the potential of computational and statistical analysis of molecular probes to improve the understanding of the cell and the possibilities of molecular medicine, Finding new insights into the molecular basis of biological processes and searching for new drugs and treatments is a problem of high complexity and where the techniques of molecular biology has been applied for many decades.

Data analysis types for biomedical applications

• Gene Selection – in data mining terms this is a process of attribute selection, which finds the genes most strongly related to a particular class
• Classification – classifying diseases or predicting outcomes based on gene expression patterns, and perhaps even identifying the best treatment for given genetic signature
• Clustering – finding new biological classes or refining existing ones

Challenges of microarray data mining

Analysis of microarrays presents a number of unique challenges for data mining. Typical data mining applications in domains like banking or web, have a large number of records (thousands and sometimes millions), while the number of fields is much smaller (at most several hundred). In contrast, a typical microarray data analysis study may have only a small number of records (less than a hundred), while the number of fields, corresponding to the number of genes, is typically in thousands. Given the difficulty of collecting microarray samples, the number of samples is likely to remain small in many interesting cases. However, having so many fields relative to so few samples, creates a high likelihood of finding “false positives” that are due to chance – both in finding differentially expressed genes, and in building predictive models. We need especially robust methods to validate the models and assess their likelihood.

Furture of microarrays data mining

Microarrays are a revolutionary new technology with great potential to provide accurate medical diagnostics, help find the right treatment and cure for many diseases and provide a detailed genome-wide molecular portrait of cellular states. The initial results are very promising and extend the possibilities of applying computational analysis and data mining to aid research in biology and medicine.