Annotating publicly-available samples and studies using interpretable modeling of unstructured metadata.

ABSTRACT: Reusing massive collections of publicly available biomedical data can significantly impact knowledge discovery. However, these public samples and studies are typically described using unstructured plain text, hindering the findability and further reuse of the data. To combat this problem, we propose txt2onto 2.0, a general-purpose method based on natural language processing and machine learning for annotating biomedical unstructured metadata to controlled vocabularies of diseases and tissues. Compared to the previous version (txt2onto 1.0), which uses numerical embeddings as features, this new version uses words as features, resulting in improved interpretability and performance, especially when few positive training instances are available. Txt2onto 2.0 uses embeddings from a large langua

SUBMITTER: Yuan H 

PROVIDER: S-EPMC11663484 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature