finding microarray genes using go ontology
Bioinformatics is the science of managing, mining and interpreting information from biological sequences and structures. DNA Microarrays, also known as gene chips, provide an effective tool for monitoring and profiling gene expression patterns by measuring the expression levels of thousands of genes simultaneously. Clustering is a popular technique for microarray data to finding groups of genes with similar functionalities based on GO Ontology. In this paper, data mining technique, clustering is used on microarray data to group genes with similar functionalities based on Go ontology. Gene Ontology is used to provide external validation for the clusters to determine if the genes in a cluster belong to a specific Biological Process, Cellular Component and Molecular Function. A functionally meaningful cluster contains many genes that are annotated to a specific GO terms. To prove that each of these new cluster sets reveal biological associations that were not apparent from clustering the original gene expression data.
microarray genes go ontology clustering functionality association euclidean distance k-means analyze database kdd