Critical Review of Data Mining Techniques for Gene Expression Analysis

• Main Authors: Aouf, M., Liyanage, L., Hansen, S.
• Format: Conference Proceeding
• Language: English
• Subjects: Australia; Clustering algorithms; Clustering methods; Data mining; DNA; Gene expression; Genetics; Mathematics; RNA; Tumors
• ISSN: 2151-1802; 2151-1810
• DOI: 10.1109/ICIAFS.2008.4783954

Classification of gene expression data has been exploded in the recent years. This can aid in the development of efficient methodology in the field of bio-informatics to be used for tumours diagnosis and treatment. Data mining is an effective technique being used in this field. One of the most difficulties facing this technology is the inappropriate classification methods that examine complex structure of gene expression data. In this paper, we give a brief introduction of gene expression data with experiment and we have made a critical review of major techniques being applied in the field of gene expression data with help of data mining. It can be seen that researchers have developed various techniques for gene data classification. In addition, they may differ from one to another whereas results are still showing the need for enhancement in this field. Some of these techniques are addressed in this paper in term of advantages and disadvantages. Accordingly, the deoxyribonucleic acid (DNA) is considered as the maestro of the tumour-derived factors. Analyzing changes on the gene expression may give rise for diagnosis enhancement of affected tissues in their early stages. For that reason, an ongoing research is addressing the problem of subspace clustering methodologies suitable for high dimensional datasets and verify of the new methodologies using appropriate datasets, particularly suitable for the analysis of gene expression data. In this context, researchers have identified various limitations of these methods particularly in the areas of information integration systems, text-mining and bio-informatics.