Computational Analysis of Sage Data: Data Mining Approach

In post genomic era, with the availability of the large number of genome databases the focus of research has shifted from sequencing to eliciting knowledge from these databases. Data mining is a collection of techniques for discovering previously unknown, valid and useful patterns from large databases. This book gives insight into an application of a data mining technique, called association rule mining, in analyzing Serial Analysis of Gene Expression (SAGE) data. SAGE is a sequencing technique used for measuring the expression levels of genes. Traditional association rule mining algorithms are not suitable for mining gene expression data due to its wide structure. This book contains the description of a specialized association rule mining algorithm, called GeneExpMiner, for SAGE Data analysis. It also contains an application of the association rule mining, where the algorithm is applied to SAGE data, for identifying the co-regulated signature genes. Some open problems which can be considered for further research in this area is also provided at the conclusion. This book is intended to research scholars in the area of computational biology or bioinformatics.