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N Sevugapandi
Bharathiyar University

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Classification Algorithm for Gene Expression Graph and Manhattan Distance N Sevugapandi; C.P. Chandran
Indonesian Journal of Electrical Engineering and Computer Science Vol 5, No 2: February 2017
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v5.i2.pp472-478

Abstract

This proposed method focus on these issues by developing a novel classification algorithm by combining Gene Expression Graph (GEG) with Manhattan distance. This method will be used to express the gene expression data. Gene Expression Graph provides the optimal view about the relationship between normal and unhealthy genes. The method of using a graph-based gene expression to express gene information was first offered by the authors in [1] and [2], It will permits to construct a classifier based on an association between graphs represented for well-known classes and graphs represented for samples to evaluate. Additionally Euclidean distance is used to measure the strength of relationship which exists between the genes.
Co-Authors C.P. Chandran