Optimizing big data in bioinformatics with swarm algorithms

Research output: Chapter in Book/Report/Conference proceedingConference contribution

7 Citations (Scopus)

Abstract

This paper describes the application of swarm algorithms on bioinformatics data namely protein sequences. The big data that exists in bioinformatics domains require an intelligent method that capable to increase the performance of classification as well as discovering the knowledge. The work optimizes the big features that exist in protein sequences using the two-tier hybrid model by applying the filter and wrapper method. The use of swarm algorithm namely particle swarm optimization has improved the classification accuracy as the features are optimized prior to classification. The study also compares the performance of swarm algorithms with the standard searching method.

Original languageEnglish
Title of host publicationProceedings - 16th IEEE International Conference on Computational Science and Engineering, CSE 2013
Pages1091-1095
Number of pages5
DOIs
Publication statusPublished - 2013
Event2013 16th IEEE International Conference on Computational Science and Engineering, CSE 2013 - Sydney, NSW
Duration: 3 Dec 20135 Dec 2013

Other

Other2013 16th IEEE International Conference on Computational Science and Engineering, CSE 2013
CitySydney, NSW
Period3/12/135/12/13

Fingerprint

Bioinformatics
Proteins
Particle swarm optimization (PSO)
Big data

Keywords

  • Big data
  • Bioinformatics
  • Particle swarm optimisation
  • Protein sequences
  • Swarm intelligent

ASJC Scopus subject areas

  • Computer Science (miscellaneous)

Cite this

Abdul-Rahman, S., Abu Bakar, A., & Mohamed Hussein, Z. A. (2013). Optimizing big data in bioinformatics with swarm algorithms. In Proceedings - 16th IEEE International Conference on Computational Science and Engineering, CSE 2013 (pp. 1091-1095). [6755339] https://doi.org/10.1109/CSE.2013.158

Optimizing big data in bioinformatics with swarm algorithms. / Abdul-Rahman, Shuzlina; Abu Bakar, Azuraliza; Mohamed Hussein, Zeti Azura.

Proceedings - 16th IEEE International Conference on Computational Science and Engineering, CSE 2013. 2013. p. 1091-1095 6755339.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abdul-Rahman, S, Abu Bakar, A & Mohamed Hussein, ZA 2013, Optimizing big data in bioinformatics with swarm algorithms. in Proceedings - 16th IEEE International Conference on Computational Science and Engineering, CSE 2013., 6755339, pp. 1091-1095, 2013 16th IEEE International Conference on Computational Science and Engineering, CSE 2013, Sydney, NSW, 3/12/13. https://doi.org/10.1109/CSE.2013.158
Abdul-Rahman S, Abu Bakar A, Mohamed Hussein ZA. Optimizing big data in bioinformatics with swarm algorithms. In Proceedings - 16th IEEE International Conference on Computational Science and Engineering, CSE 2013. 2013. p. 1091-1095. 6755339 https://doi.org/10.1109/CSE.2013.158
Abdul-Rahman, Shuzlina ; Abu Bakar, Azuraliza ; Mohamed Hussein, Zeti Azura. / Optimizing big data in bioinformatics with swarm algorithms. Proceedings - 16th IEEE International Conference on Computational Science and Engineering, CSE 2013. 2013. pp. 1091-1095
@inproceedings{9cbba1cf51b546e4bcd3cb3316538832,
title = "Optimizing big data in bioinformatics with swarm algorithms",
abstract = "This paper describes the application of swarm algorithms on bioinformatics data namely protein sequences. The big data that exists in bioinformatics domains require an intelligent method that capable to increase the performance of classification as well as discovering the knowledge. The work optimizes the big features that exist in protein sequences using the two-tier hybrid model by applying the filter and wrapper method. The use of swarm algorithm namely particle swarm optimization has improved the classification accuracy as the features are optimized prior to classification. The study also compares the performance of swarm algorithms with the standard searching method.",
keywords = "Big data, Bioinformatics, Particle swarm optimisation, Protein sequences, Swarm intelligent",
author = "Shuzlina Abdul-Rahman and {Abu Bakar}, Azuraliza and {Mohamed Hussein}, {Zeti Azura}",
year = "2013",
doi = "10.1109/CSE.2013.158",
language = "English",
pages = "1091--1095",
booktitle = "Proceedings - 16th IEEE International Conference on Computational Science and Engineering, CSE 2013",

}

TY - GEN

T1 - Optimizing big data in bioinformatics with swarm algorithms

AU - Abdul-Rahman, Shuzlina

AU - Abu Bakar, Azuraliza

AU - Mohamed Hussein, Zeti Azura

PY - 2013

Y1 - 2013

N2 - This paper describes the application of swarm algorithms on bioinformatics data namely protein sequences. The big data that exists in bioinformatics domains require an intelligent method that capable to increase the performance of classification as well as discovering the knowledge. The work optimizes the big features that exist in protein sequences using the two-tier hybrid model by applying the filter and wrapper method. The use of swarm algorithm namely particle swarm optimization has improved the classification accuracy as the features are optimized prior to classification. The study also compares the performance of swarm algorithms with the standard searching method.

AB - This paper describes the application of swarm algorithms on bioinformatics data namely protein sequences. The big data that exists in bioinformatics domains require an intelligent method that capable to increase the performance of classification as well as discovering the knowledge. The work optimizes the big features that exist in protein sequences using the two-tier hybrid model by applying the filter and wrapper method. The use of swarm algorithm namely particle swarm optimization has improved the classification accuracy as the features are optimized prior to classification. The study also compares the performance of swarm algorithms with the standard searching method.

KW - Big data

KW - Bioinformatics

KW - Particle swarm optimisation

KW - Protein sequences

KW - Swarm intelligent

UR - http://www.scopus.com/inward/record.url?scp=84900374709&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84900374709&partnerID=8YFLogxK

U2 - 10.1109/CSE.2013.158

DO - 10.1109/CSE.2013.158

M3 - Conference contribution

AN - SCOPUS:84900374709

SP - 1091

EP - 1095

BT - Proceedings - 16th IEEE International Conference on Computational Science and Engineering, CSE 2013

ER -