Molecular clustering via knowledge mining from biomedical scientific corpora
| Autor: | Hasapis, Panagiotis, Ntalaperas, Dimitrios, Kannas, Christos C., Aristodimou, Aristos, Alexandrou, Dimitrios Al, Bouras, Thanassis D., Georgousopoulos, Christos, Antoniades, Athos, Pattichis, Constantinos S., Constantinou, Andreas I. |
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| Přispěvatelé: | Pattichis, Constantinos S. [0000-0003-1271-8151], Constantinou, Andreas I. [0000-0003-0365-1821] |
| Rok vydání: | 2013 |
| Předmět: |
Grammar rules
Structure (mathematical logic) 0303 health sciences Virtual screening Pattern clustering Computer science business.industry 02 engineering and technology computer.software_genre Argumentation theory 03 medical and health sciences Rule-based machine translation 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Artificial intelligence business Cluster analysis computer Knowledge mining Natural language processing 030304 developmental biology |
| Zdroj: | 13th IEEE International Conference on BioInformatics and BioEngineering, IEEE BIBE 2013 BIBE |
| DOI: | 10.1109/BIBE.2013.6701698 |
| Popis: | In 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. Sponsors: Institute of Electrical and Electronic Engineers (IEEE) Artificial Intelligence Foundation (BAIF) Conference code: 102484 |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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