Microarray Data Analysis

Microarray analysis is an emerging field, simultaneously harnessing advances in semiconductor manufacturing, biochemistry, medicine, computation, and algorithms research. Microarrays now provide a platform for an unprecedented genome-wide view of a biological sample. Microarray analysis makes use of the vast amounts of data that the microarray platform provides. It is through the intelligent combination of mathematical algorithms and clinical validation that microarray analysis provides a real opportunity to realize the goal of targeted personalized medicine. One day, the information from a single microarray might be able to tell a doctor if a patient has cancer, what type of cancer it is, what the prognosis is, and what drug to use to best fight the cancer. The foundation of this story is being built in laboratories across the world today and it starts with sound microarray analysis. Microarray analysis is a multistep process that converts raw microarray data into biomarkers for clinical use. First, noise must be removed from raw data using preprocessing methods, such as normalization and artifact removal. Clean data can then be used to select important features or to build predictive rules called classifiers. The results of feature selection and classification are lists of biomarkers that are appropriate for classifying the data into groups such as benign or malignant. These biomarkers must then be validated clinically or through knowledge-based approaches. The results of validation can then be used as feedback in order to select better features or build better classifiers. Keywords: microarray analysis; pattern recognition; bioinformatics; cancer; personalized medicine; biomarker; DNA; RNA; computational biology

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