Skip To Main Content
TurkuNlp Group on Muck Rack

TurkuNlp Group

As seen in: Bioinformatics, Oxford University Press (OUP)
Is this you? As a journalist, you can create a free Muck Rack account to customize your profile, list your contact preferences, and upload a portfolio of your best work. Claim your profile

Get in touch with TurkuNlp

Contact TurkuNlp, search articles and posts on X, monitor coverage, and track replies from one place.

Learn more about Muck Rack

Actions

Share this page

Is this you?

As a journalist, you can create a free Muck Rack account to customize your profile, list your contact preferences, and upload a portfolio of your best work.
Claim your profile

Articles

Lifestyle factors in the biomedical literature: An ontology and comprehensive resources for named entity recognition

|
By Azarbaijan Shahid Madani, TurkuNlp Group, Lars Juhl
| Bioinformatics Verified
Despite lifestyle factors (LSFs) being increasingly acknowledged in shaping individual health trajectories, particularly in chronic diseases, they have still not been systematically described in the biomedical literature. This is in part because no named entity recognition (NER) system exists, which can comprehensively detect all types of LSFs in text.
Open in Who Shared Issue with article?

STRING-ing together protein complexes: Corpus and methods for extracting physical protein interactions from the biomedical literature

|
By TurkuNlp Group, Lars Juhl, Denmark Textimi
| Bioinformatics Verified
Understanding biological processes relies heavily on curated knowledge of physical interactions between proteins. Yet, a notable gap remains between the information stored in databases of curated knowledge and the plethora of interactions documented in the scientific literature.
Open in Who Shared Issue with article?

Overview of DrugProt task at BioCreative VII: data and methods for large-scale text mining and knowledge graph generation of heterogenous chemical-protein relations | Database | Oxford Academic

|
By TurkuNlp Group
| Oxford University Press (OUP)
The volume of drug-related information stored in scientific literature is growing continuously and it is challenging to exploit it efficiently. In particular, there is a range of different types of drug–gene/protein interactions, and their systematic extraction and characterization are essential to analyze, predict and explore key biomedical properties underlying high-impact biomedical applications.
Open in Who Shared Issue with article?
See all 4 articles