Neural Networks



Recent developments in the genomics arena have resulted techniques that can produce large amounts of expression level data. One such technique is the Microarray technology that relies on the hybridization properties of nucleic acids to monitor DNA or RNA abundance on a genomic scale. Microarrays have revolutionized the study of genome by allowing researchers to study the expression of thousands of genes simultaneously for the first time. It is being predicted that this is essential to understanding the role of genes in various biological functions.

The action of discovering patterns of gene expression is closely related to correlating sequences of genes to specific biological functions, and thereby understanding the role of genes in biological functions on a genomic scale. The ability to simultaneously study thousands of genes under a host of differing conditions presents an immense challenge in the fields of computational science and data mining. New computational and data mining techniques need to be developed in order to properly comprehend and interpret expression with the above goal in mind.

ANNS are used as a solution to various problems; however, their success as an intelligent pattern recognition methodology has been most prominently advertised. The most important, and attractive, feature of ANNs is their capability of learning (generalizing) from example (extracting knowledge from data). ANNs can do this without any prespecified rules that define intelligence or represent an expert’s knowledge. This feature makes ANNs a very popular choice for gene expression analysis and sequencing. Due to their power and flexibility, ANNS have even been used as tools for relevant variable selection, which can in turn greatly increase the expert’s knowledge and understanding of the problem.
From Cell to Gene
Cells are the basic structural and functional units of life. The blueprint for all...