Algorithmic Approach for Removing the Redundancy in Diabetic Gene Categories Based on Semantic Similarity and Gene Expression Data
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Even after so much advancement in gene expression microarray technology, the main hindrance in analyzing microarray data is its limited number of samples as compared to a number of factors, which is a major impediment in revealing actual gene functionality and valuable information from the data. Analyzing gene expression data can indicate the factors which are differentially expressed in the diseased tissue. As most of these genes have no part to play in causing the disease of interest, thus, identification of disease-causing genes can reveal not just the case of the disease, but also its pathogenic mechanism. There are a lot of gene selection methods available which have the capacity to remove irrelevant genes, but most of them are not sufficient enough in removing redundancy in genes from microarray data, which increases the computational cost and decreases the classification accuracy. Combining the gene expression data with the gene ontology information can be helpful in determining the redundancy which can then be removed using the algorithm mentioned in the work. The gene list obtained after these sequential steps of the algorithm can be analyzed further to obtain the most deterministic genes responsible for type 2 diabetes.
KeywordsMicroarray technology Gene expression Diabetes Greedy algorithm Gene ontology Sematic similarity Pearson correlation GEO database
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