2019 : Hybrid K-means, fuzzy C-means, and hierarchical clustering for DNA hepatitis C virus trend mutation analysis

Dr. Tri Arief Sardjono S.T, MT.


Abstract

Every single strand of DNA consists of 10 sequences of nucleotides. These sequences cannot be separated or randomly arranged because each sequence of DNA contains a certain genomic encoding. When a virus mutates, a drug or vaccine for that virus that has been given to a patient will become useless. Therefore, there is a need for a method of analysing the likely direction of DNA mutation so that preventative measures can be adapted more quickly. RNA-type viruses are able to alter the patterns of infected DNA, which is one way for such a virus to defend itself. In this paper, we propose a new hybrid clustering method that combines K-means, fuzzy C-means, and hierarchical clustering to predict the direction of DNA mutation trends. We have combined these three different approaches in a hybrid clustering method and tested it on two data sets of 1000 isolated positive hepatitis C virus (HCV)-infected and non …