Mining impactful discoveries from the biomedical literature
This method allows the collection of a large amount of time-stamped discoveries. These can be used for LBD evaluation, alleviating the long-standing issue of inadequate evaluation. It might also pave the way for more fine-grained LBD methods, which could exploit the diversity of these past discoveries to train supervised models. Finally the dataset (or some future version of it inspired by our method) could be used as a methodological tool for systematic reviews. We provide an online exploration tool in this perspective, available at https://brainmend.adaptcentre.ie/ .
Authors
- Declan O’Sullivan (ORCID: https://orcid.org/0000-0003-1090-3548)
- Orla M. Hardiman (ORCID: https://orcid.org/0000-0003-2610-1291)
- Mark Heverin (ORCID: https://orcid.org/0000-0002-7326-4432)
- Erwan Moreau (ORCID: https://orcid.org/0000-0001-7692-526X)
Institutions
- Trinity College Dublin (IE)
Publication Details
- Journal
- BMC Bioinformatics
- Published
- 2024-09-16
- DOI
- https://doi.org/10.1186/s12859-024-05881-9
- Citations
- 2
- Primary Topic
- Biomedical Text Mining and Ontologies
- Type
- article
- Field-Weighted Citation Impact
- 0.47
Funders
- EU Joint Programme – Neurodegenerative Disease Research
- Science Foundation Ireland