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Bioinformatics and medicine in the era of deep learning
dc.contributor.author | Bacciu, Davide |
dc.contributor.author | Lisboa, Paulo J G |
dc.contributor.author | Martín, José David |
dc.contributor.author | Stoean, Ruxandra |
dc.contributor.author | Vellido Alcacena, Alfredo |
dc.contributor.other | Universitat Politècnica de Catalunya. Departament de Ciències de la Computació |
dc.date.accessioned | 2018-07-23T06:45:35Z |
dc.date.issued | 2018 |
dc.identifier.citation | Bacciu, D., Lisboa, P., Martín, J.D., Stoean, R., Vellido, A. Bioinformatics and medicine in the era of deep learning. A: European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. "ESANN 2018: 26th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning: Bruges, April 25-26-27 2018: proceedings". I6doc.com, 2018, p. 345-354. |
dc.identifier.isbn | 978-287587047-6 |
dc.identifier.other | https://arxiv.org/abs/1802.09791 |
dc.identifier.uri | http://hdl.handle.net/2117/119703 |
dc.description.abstract | Many of the current scientific advances in the life sciences have their origin in the intensive use of data for knowledge discovery. In no area this is so clear as in bioinformatics, led by technological breakthroughs in data acquisition technologies. It has been argued that bioinformatics could quickly become the field of research generating the largest data repositories, beating other data-intensive areas such as high-energy physics or astroinformatics. Over the last decade, deep learning has become a disruptive advance in machine learning, giving new live to the long-standing connectionist paradigm in artificial intelligence. Deep learning methods are ideally suited to large-scale data and, therefore, they should be ideally suited to knowledge discovery in bioinformatics and biomedicine at large. In this brief paper, we review key aspects of the application of deep learning in bioinformatics and medicine, drawing from the themes covered by the contributions to an ESANN 2018 special session devoted to this topic. |
dc.format.extent | 10 p. |
dc.language.iso | eng |
dc.publisher | I6doc.com |
dc.subject | Àrees temàtiques de la UPC::Informàtica::Aplicacions de la informàtica::Bioinformàtica |
dc.subject | Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial::Aprenentatge automàtic |
dc.subject.lcsh | Knowledge acquisition (Expert systems) |
dc.subject.lcsh | Bioinformatics |
dc.subject.lcsh | Machine learning |
dc.subject.other | Quantitative methods |
dc.subject.other | Knowledge discovery |
dc.subject.other | Data acquisition |
dc.subject.other | Deep learning |
dc.title | Bioinformatics and medicine in the era of deep learning |
dc.type | Conference report |
dc.subject.lemac | Adquisició del coneixement (Sistemes experts) |
dc.subject.lemac | Bioinformàtica |
dc.subject.lemac | Aprenentatge automàtic |
dc.description.peerreviewed | Peer Reviewed |
dc.rights.access | Restricted access - publisher's policy |
local.identifier.drac | 23267405 |
dc.description.version | Postprint (published version) |
dc.relation.projectid | info:eu-repo/grantAgreement/MINECO/TIN2016-79576-R |
dc.date.lift | 10000-01-01 |
local.citation.author | Bacciu, D.; Lisboa, P.; Martín, J.D.; Stoean, R.; Vellido, A. |
local.citation.contributor | European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning |
local.citation.publicationName | ESANN 2018: 26th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning: Bruges, April 25-26-27 2018: proceedings |
local.citation.startingPage | 345 |
local.citation.endingPage | 354 |