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dc.rights.licenseAtribución-NoComercial 4.0 Internacional
dc.contributorBarreto, Emiliano
dc.contributor.authorLugo Martínez, Luis Eduardo
dc.date.accessioned2019-07-03T07:26:06Z
dc.date.available2019-07-03T07:26:06Z
dc.date.issued2018-09
dc.identifier.urihttps://repositorio.unal.edu.co/handle/unal/68663
dc.description.abstractThe classification of bacteria plays an essential role in multiple areas of research. Those areas include experimental biology, food and water industries, pathology, microbiology, and evolutionary studies. Although there exist methodologies for classification - such as mass spectrometry, single-nucleotide polymorphisms, microscopic morphology, and neural network approaches - a transition to a whole genome sequence based taxonomy is already undergoing. Next Generation Sequencing helps the transition by producing DNA sequence data efficiently. However, the rate of DNA sequence data generation and the high dimensionality of such data need faster computer methodologies. Machine learning, an area of artificial intelligence, has the ability to analyze high dimensional data in a systematic, fast, and efficient way. Therefore, we propose a sequential deep learning model for bacteria classification. The proposed neural network exploits the vast amounts of information generated by Next Generation Sequencing, in order to extract a classification model for whole genome bacteria sequences. A distributed representation based on k-mers of k={3,4,5} provided an efficient encoding for the bacterial sequences. The classification model relies on a bidirectional recurrent neural network architecture. It generates an accuracy of 0.99455 +/- 0.00281 for 14 species, 0.95031 +/- 0.00469 for 48 species, and 0.89107 +/- 0.00392 for 111 species. After validating the classification model, the bidirectional recurrent neural network outperformed other classification approaches, such as Naive Bayes and Feedforward neural network. The proposed model provides an automated identification method. It infers species for bacterial whole genome sequences and it does not require any manual feature extraction.
dc.format.mimetypeapplication/pdf
dc.language.isospa
dc.relation.ispartofUniversidad Nacional de Colombia Sede Bogotá Facultad de Ingeniería Departamento de Ingeniería de Sistemas e Industrial
dc.relation.ispartofDepartamento de Ingeniería de Sistemas e Industrial
dc.rightsDerechos reservados - Universidad Nacional de Colombia
dc.rights.urihttp://creativecommons.org/licenses/by-nc/4.0/
dc.subject.ddc0 Generalidades / Computer science, information and general works
dc.subject.ddc5 Ciencias naturales y matemáticas / Science
dc.subject.ddc6 Tecnología (ciencias aplicadas) / Technology
dc.subject.ddc62 Ingeniería y operaciones afines / Engineering
dc.titleA Recurrent Neural Network approach for whole genome bacteria classification
dc.typeTrabajo de grado - Maestría
dc.type.driverinfo:eu-repo/semantics/masterThesis
dc.type.versioninfo:eu-repo/semantics/acceptedVersion
dc.identifier.eprintshttp://bdigital.unal.edu.co/69758/
dc.description.degreelevelMaestría
dc.relation.referencesLugo Martínez, Luis Eduardo (2018) A Recurrent Neural Network approach for whole genome bacteria classification. Maestría thesis, Universidad Nacional de Colombia - Sede Bogotá.
dc.rights.accessrightsinfo:eu-repo/semantics/openAccess
dc.subject.proposalRecurrent neural network
dc.subject.proposalBacteria identification
dc.subject.proposalWhole genome sequence
dc.type.coarhttp://purl.org/coar/resource_type/c_bdcc
dc.type.coarversionhttp://purl.org/coar/version/c_ab4af688f83e57aa
dc.type.contentText
dc.type.redcolhttp://purl.org/redcol/resource_type/TM
oaire.accessrightshttp://purl.org/coar/access_right/c_abf2


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Atribución-NoComercial 4.0 InternacionalThis work is licensed under a Creative Commons Reconocimiento-NoComercial 4.0.This document has been deposited by the author (s) under the following certificate of deposit