APLICATION OF DATA MINING ALGORIHTMS IN MICROARRAY DATASET OF DIABETIC PATIENTS

Abstract

In recent years, rapid developments in genomics and proteomics have generated a large amount of biological data. Drawing conclusions from these data requires sophisticated computational analyses. Bioinformatics, or computational biology, is the interdisciplinary science of interpreting biological data using information technology and computer science. )The importance of this new field of inquiry will grow as we continue to generate and integrate large quantities of genomic, proteomic, and other data(.

Until recently, biology lacked the tools to analyze massive repositories of information such as the human genome database .The data mining techniques are effectively used to extract meaningful relationships from these data. Data mining is especially used in microarray analysis, which is used to study the activity of different cells under different conditions. Microarrays are one of the latest breakthroughs in experimental molecular biology that allow monitoring the expression levels of tens of thousands of genes simultaneously.

In our study scheme, we applied the Feature Selection Algorithm(FSA) on 16000 genes taken 6 sample , The result was1732genes ordered according to significance. The result was again applied on the Neural Network(NN) then on Principal Component Analysis(PCA).We then matched the results to extract the common genes that may identify the genes responsible for cardiovascular diseases.