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dc.contributor.authorHasapis, Panagiotisen
dc.contributor.authorNtalaperas, Dimitriosen
dc.contributor.authorKannas, Christos C.en
dc.contributor.authorAristodimou, Aristosen
dc.contributor.authorAlexandrou, Dimitrios Alen
dc.contributor.authorBouras, Thanassis D.en
dc.contributor.authorGeorgousopoulos, Christosen
dc.contributor.authorAntoniades, Athosen
dc.contributor.authorPattichis, Constantinos S.en
dc.contributor.authorConstantinou, Andreas I.en
dc.creatorHasapis, Panagiotisen
dc.creatorNtalaperas, Dimitriosen
dc.creatorKannas, Christos C.en
dc.creatorAristodimou, Aristosen
dc.creatorAlexandrou, Dimitrios Alen
dc.creatorBouras, Thanassis D.en
dc.creatorGeorgousopoulos, Christosen
dc.creatorAntoniades, Athosen
dc.creatorPattichis, Constantinos S.en
dc.creatorConstantinou, Andreas I.en
dc.date.accessioned2019-11-13T10:40:21Z
dc.date.available2019-11-13T10:40:21Z
dc.date.issued2013
dc.identifier.isbn978-1-4799-3163-7
dc.identifier.urihttp://gnosis.library.ucy.ac.cy/handle/7/54078
dc.description.abstractIn this paper, an architecture is presented that allows the extraction of argumentation clauses that might exist in publications, in order to perform molecular clustering on referenced molecules. Grammar rules are defined and used to identify sentences corresponding to argumentation being present in publications. The references of those molecules are then compiled as lists that include their structure definition in SMILES format. These lists are given as input to virtual screening tools and then to a molecular clustering tool, with the ultimate goal to classify molecules that are known to be prone to specific diseases, thus leading to the discovery of new drugs. © 2013 IEEE.en
dc.source13th IEEE International Conference on BioInformatics and BioEngineering, IEEE BIBE 2013en
dc.source13th IEEE International Conference on BioInformatics and BioEngineering, IEEE BIBE 2013en
dc.source.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-84894157128&doi=10.1109%2fBIBE.2013.6701698&partnerID=40&md5=44c7225d1cc5dff209d63c9f0a410bf3
dc.titleMolecular clustering via knowledge mining from biomedical scientific corporaAAAen
dc.typeinfo:eu-repo/semantics/conferenceObject
dc.identifier.doi10.1109/BIBE.2013.6701698
dc.author.faculty002 Σχολή Θετικών και Εφαρμοσμένων Επιστημών / Faculty of Pure and Applied Sciences
dc.author.departmentΤμήμα Πληροφορικής / Department of Computer Science
dc.type.uhtypeConference Objecten
dc.description.notes<p>Sponsors: Institute of Electrical and Electronic Engineers (IEEE)en
dc.description.notesArtificial Intelligence Foundation (BAIF)en
dc.description.notesConference code: 102484</p>en
dc.contributor.orcidPattichis, Constantinos S. [0000-0003-1271-8151]
dc.contributor.orcidConstantinou, Andreas I. [0000-0003-0365-1821]
dc.gnosis.orcid0000-0003-1271-8151
dc.gnosis.orcid0000-0003-0365-1821


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