Brunak, Søren

Calvo, Diego

de la Torre, Isabel

Franco, Manuel Angel

Gonzalez-Izarzugaza, Jose Maria

Online Research Database In Technology

                       General rights Copyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights.  • Users may download and print one copy of any publication from the public portal for the purpose of private study or research. • You may not further distribute the material or use it for any profit-making activity or commercial gain • You may freely distribute the URL identifying the publication in the public portal   If you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediately and investigate your claim.     Downloaded from orbit.dtu.dk on: Jan 01, 2018Deep feature learning for virus detection using a Convolutional Neural NetworkCalvo, Diego ; de la Torre, Isabel ; Franco, Manuel Angel; Brunak, Søren; Gonzalez-Izarzugaza, JoseMariaPublished in:Book of Abstracts, Sustain 2017Publication date:2017Document VersionPublisher's PDF, also known as Version of recordLink back to DTU OrbitCitation (APA):Calvo, D., de la Torre, I., Franco, M. A., Brunak, S., & Gonzalez-Izarzugaza, J. M. (2017). Deep feature learningfor virus detection using a Convolutional Neural Network. In Book of Abstracts, Sustain 2017 [H-6] TechnicalUniversity of Denmark (DTU).Deep	feature	learning	for	virus	detection	using	a	Convolutional	Neural	NetworkDiego	Calvo1,	2,	Isabel de la Torre2, Manuel Angel Franco 2, Søren	Brunak1,	José	M.G.	Izarzugaza*1	1:	Department	of	Bioinformatics,	Technical	University	of	Denmark,	Kgs.	Lyngby,	Denmark.		2:	Department	of	Signal	Theory	and	Communications,	University	of	Valladolid,	Valladolid,	Spain.	*Corresponding	author	e-mail:	txema@bioinformatics.dtu.dkThis	study	is	focused	on	the	development	of	a	technology	to	identify	characteristics	in	nucleotide	sequences	using	deep	learning	provided	by	Convolutional	Neural	Networks.	In	order	to	demonstrate	the	effectiveness	of	this	technology,	a	classifier	has	been	developed	to	identify	viruses	in	sequencing	reads	of	100	nucleotides,	a	proxy	for	a	real	NGS	scenario.	This	classifier	is	able	to	search	for	known	virus	characteristics	and	identify	potential	new	viruses	that	are	currently	undetected.	As	it	is	not	necessary	to	read	the	complete	sequences	to	recognize	a	virus,	we	manage	to	reduce	the	time	and	costs	of	virus	identification.	The	used	Convolutional	Neural	Network	 to	develop	 the	classifier	has	been	trained	with	RefSeq	data.	The	training	set	was	made	up	of	two	subsets.	The	first	subset	(positive	set)	includes	all	the	nucleotides	sequences	of	found	viruses	in	the	database	and	the	second	subset	(negative	set)	is	composed	by	a	random	selection	of	all	the	nucleotide	sequences	of	non-viruses	respecting	the	existing	proportion	of	each	found	specie.		This	 training	 group	 undergoes	 is	 partitioning,	 overlapping	 and	 data	 cleaning	 transformations	 and	 it	 has	resulted	in	a	training	set	of	39.807.052	elements	of	approximately	2.2Gb	of	storage.			Sustain Abstract H-6

Deep feature learning for virus detection using a Convolutional Neural Network

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Deep feature learning for virus detection using a Convolutional Neural Network

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