Pattern matching performance comparisons as big data analysis recommendations for Hepatitis C Virus (HCV) sequence DNA

Berlian Al Kindhi*, Tri Arief Sardjono

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

9 Citations (Scopus)

Abstract

A data bank can provide very useful information while mined properly.[27] In order to be optimally extracted, data mining can be done by observing capacity and characteristics of the data; so it can generates Knowledge Discovery in Databases as expected. For instance in Gene Bank, every single record of DNA, there are at least ten thousand sequences recorded. If the data is more than a hundred records, it will be a big sequence of data to be processed. Hepatitis C Virus (HCV) is a liver disease which can infect humans through blood. HCV infection can be asymptomatic, or it can be hepatitis acute, chronic, furthermore cirrhosis. Hepatitis C is generally does not show symptoms in the early stages. About 75 percent people with hepatitis C did not realize that they had infected until liver damage years later. Therefore needed a sequences DNA Mining is needed to analyse the DNA history whether it is infected by HCV or not. This study compares several methods of string matching to discover which methods have the best performance in processing DNA mining. In addition, this study also analyzed DNA HCV genetic mutations trend as a Knowledege Discovery in Database in DNA mining.

Original languageEnglish
Title of host publicationProceedings - AIMS 2015, 3rd International Conference on Artificial Intelligence, Modelling and Simulation
EditorsMohd Hanafi Ahmad Hijazi, Ismail Saad, David Al-Dabass, Nurmin Bolong
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages99-104
Number of pages6
ISBN (Electronic)9781467386753
DOIs
Publication statusPublished - 20 Oct 2016
Event3rd International Conference on Artificial Intelligence, Modelling and Simulation, AIMS 2015 - Kota Kinabalu, Sabah, Malaysia
Duration: 2 Dec 20154 Dec 2015

Publication series

NameProceedings - AIMS 2015, 3rd International Conference on Artificial Intelligence, Modelling and Simulation

Conference

Conference3rd International Conference on Artificial Intelligence, Modelling and Simulation, AIMS 2015
Country/TerritoryMalaysia
CityKota Kinabalu, Sabah
Period2/12/154/12/15

Keywords

  • Boyer Moore
  • Data Mining
  • Knuth Morris-Path

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