Automated annotation of scientific texts for ML-based keyphrase extraction and validation

Database (Oxford) 2025-01-20

Summary:

Advanced omics technologies and facilities generate a wealth of valuable data daily; however, the data often lack the essential metadata required for researchers to find, curate, and search them effectively. The lack of metadata poses a significant challenge in the utilization of these data sets. Machine learning (ML)-based metadata extraction techniques have emerged as a potentially viable approach to automatically annotating scientific data sets with the metadata necessary for enabling...

Link:

https://pubmed.ncbi.nlm.nih.gov/39331731/?utm_source=Other&utm_medium=rss&utm_campaign=journals&utm_content=101517697&fc=None&ff=20250120190630&v=2.18.0.post9+e462414

From feeds:

📚BioDBS Bibliography » Database (Oxford)

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Authors:

Oluwamayowa O Amusat, Harshad Hegde, Christopher J Mungall, Anna Giannakou, Neil P Byers, Dan Gunter, Kjiersten Fagnan, Lavanya Ramakrishnan

Date tagged:

01/20/2025, 19:08

Date published:

09/27/2024, 06:00