MEDOBO: 18 Million+ MEDLINE Abstracts Tagged with OBO Ontologies

Abstract

Motivation: The number of biomedical and life science publications is rapidly increasing, with MEDLINE alone having over 30 million citations. The massive amount of information highlights the need for biomedical text analytics to improve the accessibility of bio-data. However, the presence of multiple subdomains within life sciences poses a significant challenge. To address this, an automatic classification system is required that can annotate texts with relevant subdomains in a text analytics pipeline. This will make text processing more efficient and precise for subsequent modules in the pipeline by reducing their search space to concepts within a limited set of subdomains. The development of text classification systems for life sciences subdomains requires training data as the first requirement, but unfortunately, a high-quality and large-scale dataset for this task is lacking. Results: We present MEDOBO, a dataset comprising over 18 million MEDLINE abstracts that have been automatically labeled with ontologies from the Open Biological and Biomedical Ontology (OBO) Foundry using Medical Subject Headings (MeSH). Our dataset has achieved a conformity score of over 91% F1 when compared to an expert-annotated dataset. We have trained multiple models on MEDOBO to evaluate distribution and classification difficulty and the results indicate significant potential for enhancing multi-label text classification.