Neural Networks


With the dawn of the genome era computational methods in the automatic analysis of biological data have become increasingly important. The explosion in the production rate of expression level data has highlighted the need for automated techniques that help scientists analyze, understand, and cluster (sequence) the enormous amount of data that is being produced. Example of such problems are analyzing gene expression level data produced by microarray technology on the genomic scale, sequencing genes on the genomic scale, sequencing proteins and amino acids, etc. Researchers have recognised Artificial Neural Networks as a promising technique that can be applied to several problems in genome informatics and molecular sequence analysis. This seminar explains how Neural Networks have been widely employed in genome informatics research.

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