Implementation of Tribe Markov Clustering Using Sparse Matrix for Dengue Virus Clusterization
Ibnu Hadi (a), Selly Anastassia Amellia Kharis (a*), Sudarwanto (a)

a) Faculty of Mathematics and Science, Universitas Negeri Jakarta
Jalan Rawamangun Muka, Jakarta 13220, Indonesia
*sellyanas[at]yahoo.com

Abstract

Dengue virus is a member of the genus Flavivirus of the family Flaviviridae. The global incidence of dengue has grown dramatically in recent decades. The dengue virus has 10 viral proteins, 3 structural proteins and 7 nonstructural proteins. To perform the molecular functions required for invasion, replication, and spread of the virus, proteins encoded by dengue virus must interact with and alter the behavior of protein networks in both hosts. In this paper, we present a clustering dengue virus based on Tribe Markov Clustering (T-MCL). T-MCL is a graph clustering method which is the modification of Markov Clustering Algorithm (MCL). T-MCL process is built using R programming language is applied to PPI networks of 26 dengue virus genes data obtained from Virus Pathogen Database and Analysis Resource (ViPR) in 2010-2014. Because data processed in bioinformatics usually have a vast amount of information and have high sparsity, a method to save memory usage and make the computing process faster is needed. This research concludes that T-MCL method produces 7 groups of 26 dengue viral protein sequences with groups having one or more group centers using sparse matrix.

Keywords: Tribe Markov Clustering; Sparse Matrix; Dengue Virus

Topic: Mathematics

Link: https://ifory.id/abstract-plain/wU6ZATKFRDcj

Web Format | Corresponding Author (Selly Anastassia Amellia Kharis)