Vacceed: A high-throughput in silico vaccine candidate discovery pipeline for eukaryotic pathogens based on reverse vaccinology

Goodswen, SJ; Kennedy, PJ; Ellis, JT

Vacceed: A high-throughput in silico vaccine candidate discovery pipeline for eukaryotic pathogens based on reverse vaccinology

Goodswen, SJ Kennedy, PJ

Ellis, JT

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Publication Type:: Journal Article
Citation:: Bioinformatics, 2014, 30 (16), pp. 2381 - 2383
Issue Date:: 2014-08-15

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Field	Value	Language
dc.contributor.author	Goodswen, SJ	en_US
dc.contributor.author	Kennedy, PJ https://orcid.org/0000-0001-7837-3171	en_US
dc.contributor.author	Ellis, JT https://orcid.org/0000-0001-7328-4831	en_US
dc.date.issued	2014-08-15	en_US
dc.identifier.citation	Bioinformatics, 2014, 30 (16), pp. 2381 - 2383	en_US
dc.identifier.issn	1367-4803	en_US
dc.identifier.uri	http://hdl.handle.net/10453/34138
dc.description.abstract	Summary: We present Vacceed, a highly configurable and scalable framework designed to automate the process of high-throughput in silico vaccine candidate discovery for eukaryotic pathogens. Given thousands of protein sequences from the target pathogen as input, the main output is a ranked list of protein candidates determined by a set of machine learning algorithms. Vacceed has the potential to save time and money by reducing the number of false candidates allocated for laboratory validation. Vacceed, if required, can also predict protein sequences from the pathogen's genome. © The Author 2014.	en_US
dc.relation.ispartof	Bioinformatics	en_US
dc.relation.isbasedon	10.1093/bioinformatics/btu300	en_US
dc.subject.classification	Bioinformatics	en_US
dc.subject.mesh	Vaccines	en_US
dc.subject.mesh	Sequence Analysis, Protein	en_US
dc.subject.mesh	Algorithms	en_US
dc.subject.mesh	Artificial Intelligence	en_US
dc.subject.mesh	Computer Simulation	en_US
dc.subject.mesh	Software	en_US
dc.title	Vacceed: A high-throughput in silico vaccine candidate discovery pipeline for eukaryotic pathogens based on reverse vaccinology	en_US
dc.type	Journal Article
utslib.citation.volume	16	en_US
utslib.citation.volume	30	en_US
utslib.for	060102 Bioinformatics	en_US
utslib.for	01 Mathematical Sciences	en_US
utslib.for	06 Biological Sciences	en_US
utslib.for	08 Information and Computing Sciences	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
pubs.organisational-group	/University of Technology Sydney/Faculty of Science
pubs.organisational-group	/University of Technology Sydney/Faculty of Science/School of Life Sciences
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
pubs.organisational-group	/University of Technology Sydney/Strength - CHT - Health Technologies
utslib.copyright.status	open_access
pubs.issue	16	en_US
pubs.publication-status	Published	en_US
pubs.volume	30	en_US

Abstract:

Summary: We present Vacceed, a highly configurable and scalable framework designed to automate the process of high-throughput in silico vaccine candidate discovery for eukaryotic pathogens. Given thousands of protein sequences from the target pathogen as input, the main output is a ranked list of protein candidates determined by a set of machine learning algorithms. Vacceed has the potential to save time and money by reducing the number of false candidates allocated for laboratory validation. Vacceed, if required, can also predict protein sequences from the pathogen's genome. © The Author 2014.

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http://hdl.handle.net/10453/34138