Distinguishing medical drugs from a large set of side effects using a distributed genetic algorithm on a PC cluster

Fazal Noor; Majed Alhaisoni; Mashaan A. Alshammari; Ravi P. Ramachandran

doi:10.1109/ISCAS.2015.7168752

Distinguishing medical drugs from a large set of side effects using a distributed genetic algorithm on a PC cluster

Fazal Noor, Majed Alhaisoni, Mashaan A. Alshammari, Ravi P. Ramachandran

Electrical Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

A Distributed Genetic Algorithm to compute minimal reducts is presented for a novel biomedical application to distinguish 50 medical drugs from 228 side effects. The results indicate that 15 side effects are sufficient to differentiate among all the 50 drugs. In fact, any one of 4 sets of 15 side effects can be used. The Distributed Genetic Algorithm is inherently parallel, uses a variable mutation rate and is efficiently implemented on a PC cluster using 5, 10 and 20 nodes each with a Message Passing Interface. Results show that the distributed algorithm with 20 nodes uses much less computation time than two sequential methods (savings of about a factor of 5).

Original language	English (US)
Title of host publication	2015 IEEE International Symposium on Circuits and Systems, ISCAS 2015
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	790-793
Number of pages	4
ISBN (Electronic)	9781479983919
DOIs	https://doi.org/10.1109/ISCAS.2015.7168752
State	Published - Jul 27 2015
Event	IEEE International Symposium on Circuits and Systems, ISCAS 2015 - Lisbon, Portugal Duration: May 24 2015 → May 27 2015

Publication series

Name	Proceedings - IEEE International Symposium on Circuits and Systems
Volume	2015-July
ISSN (Print)	0271-4310

Other

Other	IEEE International Symposium on Circuits and Systems, ISCAS 2015
Country/Territory	Portugal
City	Lisbon
Period	5/24/15 → 5/27/15

All Science Journal Classification (ASJC) codes

Electrical and Electronic Engineering

Access to Document

10.1109/ISCAS.2015.7168752

Cite this

Noor, F., Alhaisoni, M., Alshammari, M. A., & Ramachandran, R. P. (2015). Distinguishing medical drugs from a large set of side effects using a distributed genetic algorithm on a PC cluster. In 2015 IEEE International Symposium on Circuits and Systems, ISCAS 2015 (pp. 790-793). Article 7168752 (Proceedings - IEEE International Symposium on Circuits and Systems; Vol. 2015-July). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ISCAS.2015.7168752

Noor, Fazal ; Alhaisoni, Majed ; Alshammari, Mashaan A. et al. / Distinguishing medical drugs from a large set of side effects using a distributed genetic algorithm on a PC cluster. 2015 IEEE International Symposium on Circuits and Systems, ISCAS 2015. Institute of Electrical and Electronics Engineers Inc., 2015. pp. 790-793 (Proceedings - IEEE International Symposium on Circuits and Systems).

@inproceedings{38cbae2267904eaaafcd4b45c9ca7309,

title = "Distinguishing medical drugs from a large set of side effects using a distributed genetic algorithm on a PC cluster",

abstract = "A Distributed Genetic Algorithm to compute minimal reducts is presented for a novel biomedical application to distinguish 50 medical drugs from 228 side effects. The results indicate that 15 side effects are sufficient to differentiate among all the 50 drugs. In fact, any one of 4 sets of 15 side effects can be used. The Distributed Genetic Algorithm is inherently parallel, uses a variable mutation rate and is efficiently implemented on a PC cluster using 5, 10 and 20 nodes each with a Message Passing Interface. Results show that the distributed algorithm with 20 nodes uses much less computation time than two sequential methods (savings of about a factor of 5).",

author = "Fazal Noor and Majed Alhaisoni and Alshammari, {Mashaan A.} and Ramachandran, {Ravi P.}",

note = "Publisher Copyright: {\textcopyright} 2015 IEEE.; IEEE International Symposium on Circuits and Systems, ISCAS 2015 ; Conference date: 24-05-2015 Through 27-05-2015",

year = "2015",

month = jul,

day = "27",

doi = "10.1109/ISCAS.2015.7168752",

language = "English (US)",

series = "Proceedings - IEEE International Symposium on Circuits and Systems",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "790--793",

booktitle = "2015 IEEE International Symposium on Circuits and Systems, ISCAS 2015",

address = "United States",

}

Noor, F, Alhaisoni, M, Alshammari, MA & Ramachandran, RP 2015, Distinguishing medical drugs from a large set of side effects using a distributed genetic algorithm on a PC cluster. in 2015 IEEE International Symposium on Circuits and Systems, ISCAS 2015., 7168752, Proceedings - IEEE International Symposium on Circuits and Systems, vol. 2015-July, Institute of Electrical and Electronics Engineers Inc., pp. 790-793, IEEE International Symposium on Circuits and Systems, ISCAS 2015, Lisbon, Portugal, 5/24/15. https://doi.org/10.1109/ISCAS.2015.7168752

Distinguishing medical drugs from a large set of side effects using a distributed genetic algorithm on a PC cluster. / Noor, Fazal; Alhaisoni, Majed; Alshammari, Mashaan A. et al.
2015 IEEE International Symposium on Circuits and Systems, ISCAS 2015. Institute of Electrical and Electronics Engineers Inc., 2015. p. 790-793 7168752 (Proceedings - IEEE International Symposium on Circuits and Systems; Vol. 2015-July).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Distinguishing medical drugs from a large set of side effects using a distributed genetic algorithm on a PC cluster

AU - Noor, Fazal

AU - Alhaisoni, Majed

AU - Alshammari, Mashaan A.

AU - Ramachandran, Ravi P.

PY - 2015/7/27

Y1 - 2015/7/27

N2 - A Distributed Genetic Algorithm to compute minimal reducts is presented for a novel biomedical application to distinguish 50 medical drugs from 228 side effects. The results indicate that 15 side effects are sufficient to differentiate among all the 50 drugs. In fact, any one of 4 sets of 15 side effects can be used. The Distributed Genetic Algorithm is inherently parallel, uses a variable mutation rate and is efficiently implemented on a PC cluster using 5, 10 and 20 nodes each with a Message Passing Interface. Results show that the distributed algorithm with 20 nodes uses much less computation time than two sequential methods (savings of about a factor of 5).

AB - A Distributed Genetic Algorithm to compute minimal reducts is presented for a novel biomedical application to distinguish 50 medical drugs from 228 side effects. The results indicate that 15 side effects are sufficient to differentiate among all the 50 drugs. In fact, any one of 4 sets of 15 side effects can be used. The Distributed Genetic Algorithm is inherently parallel, uses a variable mutation rate and is efficiently implemented on a PC cluster using 5, 10 and 20 nodes each with a Message Passing Interface. Results show that the distributed algorithm with 20 nodes uses much less computation time than two sequential methods (savings of about a factor of 5).

UR - http://www.scopus.com/inward/record.url?scp=84946224078&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84946224078&partnerID=8YFLogxK

U2 - 10.1109/ISCAS.2015.7168752

DO - 10.1109/ISCAS.2015.7168752

M3 - Conference contribution

AN - SCOPUS:84946224078

T3 - Proceedings - IEEE International Symposium on Circuits and Systems

SP - 790

EP - 793

BT - 2015 IEEE International Symposium on Circuits and Systems, ISCAS 2015

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - IEEE International Symposium on Circuits and Systems, ISCAS 2015

Y2 - 24 May 2015 through 27 May 2015

ER -

Noor F, Alhaisoni M, Alshammari MA, Ramachandran RP. Distinguishing medical drugs from a large set of side effects using a distributed genetic algorithm on a PC cluster. In 2015 IEEE International Symposium on Circuits and Systems, ISCAS 2015. Institute of Electrical and Electronics Engineers Inc. 2015. p. 790-793. 7168752. (Proceedings - IEEE International Symposium on Circuits and Systems). doi: 10.1109/ISCAS.2015.7168752

Distinguishing medical drugs from a large set of side effects using a distributed genetic algorithm on a PC cluster

Abstract

Publication series

Other

All Science Journal Classification (ASJC) codes

Access to Document

Other files and links

Fingerprint

Cite this