Predictive vaccinology: Optimisation of predictions using support vector machine classifiers

Ivana Bozic; Guang Lan Zhang; Vladimir Brusic

doi:10.1007/11508069_49

Predictive vaccinology: Optimisation of predictions using support vector machine classifiers

Ivana Bozic, Guang Lan Zhang, Vladimir Brusic

Research output: Journal Publication › Conference article › peer-review

21 Citations (Scopus)

Abstract

Promiscuous human leukocyte antigen (HLA) binding peptides are ideal targets for vaccine development. Existing computational models for prediction of promiscuous peptides used hidden Markov models and artificial neural networks as prediction algorithms. We report a system based on support vector machines that outperforms previously published methods. Preliminary testing showed that it can predict peptides binding to HLA-A2 and -A3 supertype molecules with excellent accuracy, even for molecules where no binding data are currently available.

Original language	English
Pages (from-to)	375-381
Number of pages	7
Journal	Lecture Notes in Computer Science
Volume	3578
DOIs	https://doi.org/10.1007/11508069_49
Publication status	Published - 2005
Externally published	Yes
Event	6th International Conference on Intelligent Data Engineering and Automated Learning - IDEAL 2005 - Brisbane, Australia Duration: 6 Jul 2005 → 8 Jul 2005

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1007/11508069_49

Cite this

@article{b6f6cb0815c24cc5a662ab1537664a25,

title = "Predictive vaccinology: Optimisation of predictions using support vector machine classifiers",

abstract = "Promiscuous human leukocyte antigen (HLA) binding peptides are ideal targets for vaccine development. Existing computational models for prediction of promiscuous peptides used hidden Markov models and artificial neural networks as prediction algorithms. We report a system based on support vector machines that outperforms previously published methods. Preliminary testing showed that it can predict peptides binding to HLA-A2 and -A3 supertype molecules with excellent accuracy, even for molecules where no binding data are currently available.",

author = "Ivana Bozic and Zhang, {Guang Lan} and Vladimir Brusic",

year = "2005",

doi = "10.1007/11508069_49",

language = "English",

volume = "3578",

pages = "375--381",

journal = "Lecture Notes in Computer Science",

issn = "0302-9743",

publisher = "Springer Verlag",

note = "6th International Conference on Intelligent Data Engineering and Automated Learning - IDEAL 2005 ; Conference date: 06-07-2005 Through 08-07-2005",

}

TY - JOUR

T1 - Predictive vaccinology

T2 - 6th International Conference on Intelligent Data Engineering and Automated Learning - IDEAL 2005

AU - Bozic, Ivana

AU - Zhang, Guang Lan

AU - Brusic, Vladimir

PY - 2005

Y1 - 2005

N2 - Promiscuous human leukocyte antigen (HLA) binding peptides are ideal targets for vaccine development. Existing computational models for prediction of promiscuous peptides used hidden Markov models and artificial neural networks as prediction algorithms. We report a system based on support vector machines that outperforms previously published methods. Preliminary testing showed that it can predict peptides binding to HLA-A2 and -A3 supertype molecules with excellent accuracy, even for molecules where no binding data are currently available.

AB - Promiscuous human leukocyte antigen (HLA) binding peptides are ideal targets for vaccine development. Existing computational models for prediction of promiscuous peptides used hidden Markov models and artificial neural networks as prediction algorithms. We report a system based on support vector machines that outperforms previously published methods. Preliminary testing showed that it can predict peptides binding to HLA-A2 and -A3 supertype molecules with excellent accuracy, even for molecules where no binding data are currently available.

UR - http://www.scopus.com/inward/record.url?scp=26444499469&partnerID=8YFLogxK

U2 - 10.1007/11508069_49

DO - 10.1007/11508069_49

M3 - Conference article

AN - SCOPUS:26444499469

SN - 0302-9743

VL - 3578

SP - 375

EP - 381

JO - Lecture Notes in Computer Science

JF - Lecture Notes in Computer Science

Y2 - 6 July 2005 through 8 July 2005

ER -

Predictive vaccinology: Optimisation of predictions using support vector machine classifiers

Abstract

ASJC Scopus subject areas

UN SDGs

Access to Document

Other files and links

Fingerprint

Cite this