Typical Genomic Framework on Disease Analysis

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Author(s):
J. Stanly Thomas, Dr. N. Rajkumar
Published Date:
January 05, 2015
Issue:
Volume 4, Issue 5
Page(s):
11 - 15
Views:
3915
Downloads:
83

Keywords:
association rules, classification rules, very large database with similarity and density and weight based clustering analysis
Citation:
J. Stanly Thomas, Dr. N. Rajkumar, "Typical Genomic Framework on Disease Analysis ". International Journal of Research in Computer Science, 4 (5): pp. 11-15, January 2015. Other Formats

Abstract

The challenging and major role of the doctor in human life is to predict as well as diagnose the disease which has got infected in the human body. This typical genomic framework on disease analysis algorithm is designed to store and drive each and every gene characteristics like shape, weight, location and normal growth culture. Whenever the disease report is feed into this data mining algorithm triggers the similarity test built upon the data mining classification rules. A gene is usually comprised of hundreds of individual nucleotides arranged in a particular order. There are almost an unlimited number of ways that the nucleotides can be ordered and sequenced to form distinct genes. The algorithm delivers the difference between diseased and healthy status shall guide us to conclude the disease severity, stage and its nature. This powerful Typical Genomic Framework on Disease Analysis (TGFDA) algorithm is built to deliver instant result over Very Large Database using density and weight based Clustering Algorithm.

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