20.500.12556/DiRROS-16585 FooDis a food-disease relation mining pipeline FooDis: a food-disease relation mining pipeline Nowadays, it is really important and crucial to follow the new biomedical knowledge that is presented in scientific literature. To this end, Information Extraction pipelines can help to automatically extract meaningful relations from textual data that further require additional checks by domain experts. In the last two decades, a lot of work has been performed for extracting relations between phenotype and health concepts, however, the relations with food entities which are one of the most important environmental concepts have never been explored. In this study, we propose FooDis, a novel Information Extraction pipeline that employs state-of-the-art approaches in Natural Language Processing to mine abstracts of biomedical scientific papers and automatically suggests potential cause or treat relations between food and disease entities in different existing semantic resources. A comparison with already known relations indicates that the relations predicted by our pipeline match for 90% of the food-disease pairs that are common in our results and the NutriChem database, and 93% of the common pairs in the DietRx platform. The comparison also shows that the FooDis pipeline can suggest relations with high precision. The FooDis pipeline can be further used to dynamically discover new relations between food and diseases that should be checked by domain experts and further used to populate some of the existing resources used by NutriChem and DietRx. text mining relation extraction named entity recognition named entity linking food-disease relations besedilno rudarjenje luščenje povezav prepoznavanje poimenovanih enitet povezovanje poimenovanih entitet prehrana bolezni true false true Elsevier Nizozemska Angleški jezik Slovenski jezik © 2023 The Author(s). Neznano 2023-05-25 09:24:44 2023-05-25 09:24:46 2023-06-15 09:52:14 0000-00-00 00:00:00 2023 0 0 Nasl. z nasl. zaslona; Opis vira z dne 25. 5. 2023; str. 1-48 Vol. , [article no.] 102586 [in press] 2023 0000-00-00 PostprintKoncna VTisku NiDoloceno 2023-04-11 2023-05-16 2023-05-20 004.8 1873-2860 10.1016/j.artmed.2023.102586 153384451 https://www.sciencedirect.com/science/article/pii/S0933365723001008 1 793122d2-fabc-11ed-a314-001a4af901a5 https://dirros.openscience.si/Dokument.php?lang=slv&id=22431 RAZ_Cenikj_Gjorgjina_2023.pdf RAZ_Cenikj_Gjorgjina_2023.pdf 1 2502DA6E1E37E77244654DB1A30DCEBB 9c6502975ace7d219cb4d49f3bf5520249fd8a99ff9e3bccc23c91626f2db9df 2024a13e-fabe-11ed-a314-001a4af901a5 https://dirros.openscience.si/Dokument.php?lang=slv&id=22432 Institut Jožef Stefan 0 0 0