| Title: | FooDis : a food-disease relation mining pipeline |
|---|
| Authors: | ID Cenikj, Gjorgjina, Institut Jožef Stefan (Author)
ID Eftimov, Tome, Institut Jožef Stefan (Author)
ID Koroušić-Seljak, Barbara, Institut Jožef Stefan (Author) |
|---|
| Files: |  URL - Source URL, visit https://www.sciencedirect.com/science/article/pii/S0933365723001008
 PDF - Presentation file, download (1,11 MB)
MD5: 2502DA6E1E37E77244654DB1A30DCEBB
|
|---|
| Language: | English |
|---|
| Typology: | 1.01 - Original Scientific Article |
|---|
| Organization: |  IJS - Jožef Stefan Institute
|
|---|
| Abstract: | 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. |
|---|
| Keywords: | text mining, relation extraction, named entity recognition, named entity linking, food-disease relations |
|---|
| Publication status: | In print |
|---|
| Publication version: | Author Accepted Manuscript |
|---|
| Submitted for review: | 11.04.2023 |
|---|
| Article acceptance date: | 16.05.2023 |
|---|
| Publication date: | 20.05.2023 |
|---|
| Publisher: | Elsevier |
|---|
| Year of publishing: | 2023 |
|---|
| Number of pages: | str. 1-48 |
|---|
| Numbering: | Vol. , [article no.] 102586 |
|---|
| Source: | Nizozemska |
|---|
| PID: | 20.500.12556/DiRROS-16585  |
|---|
| UDC: | 004.8 |
|---|
| ISSN on article: | 1873-2860 |
|---|
| DOI: | 10.1016/j.artmed.2023.102586 |
|---|
| COBISS.SI-ID: | 153384451 |
|---|
| Copyright: | © 2023 The Author(s). |
|---|
| Note: | Nasl. z nasl. zaslona;
Opis vira z dne 25. 5. 2023;
|
|---|
| Publication date in DiRROS: | 25.05.2023 |
|---|
| Views: | 691 |
|---|
| Downloads: | 404 |
|---|
| Metadata: |    |
|---|
|
:
|
Copy citation |
|---|
| | | | Share: |  |
|---|
Hover the mouse pointer over a document title to show the abstract or click
on the title to get all document metadata. |
