Boosting biomedical document classification through the use of domain entity recognizers and semantic ontologies for document representation: The case of gluten bibliome

Pérez-Pérez, Martín; Ferreira, T. N.; Lourenço, Anália; Igrejas, Gilberto; Fdez-Riverola, Florentino

doi:10.1016/j.neucom.2021.10.100

Cited by 3 publications

(2 citation statements)

References 87 publications

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“…A side application of the MEDOBO is to assist researchers in getting the most relevant set of articles for their studies given their desired set of ontologies. Similar studies to such endeavor have already been successfully applied with a limited scope, such as curating gluten-relevant publications in [15]. In contrast, our work provides a flexible approach concerning ontologies for curating publications based on the ontology coverage ratio.…”

Section: Discussionmentioning

confidence: 95%

“…Biomedical text classification is an integral element of many applications in the biomedical and life sciences, such as automatic diagnosis coding [12], tweet classification on public health [13], semantic indexing of biomedical articles using MeSH terms [3,4], and ontology-based categorization of clinical studies [14]. Text classification in the literature may appear in three variants: Binary [15], Multi-class [6], and Multi-label [16]. A Binary text classifier assigns a positive or negative class to a document.…”

Section: Related Workmentioning

confidence: 99%

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MEDOBO: 18 Million+ MEDLINE Abstracts Tagged with OBO Ontologies

Aghaebrahimian¹,

Anisimova²,

Gil³

2023

Preprint

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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.

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Section: Discussionmentioning

confidence: 95%

Section: Related Workmentioning

confidence: 99%

MEDOBO: 18 Million+ MEDLINE Abstracts Tagged with OBO Ontologies

Aghaebrahimian¹,

Anisimova²,

Gil³

2023

Preprint

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A novel gluten knowledge base of potential biomedical and health-related interactions extracted from the literature: Using machine learning and graph analysis methodologies to reconstruct the bibliome

Pérez-Pérez

Ferreira

Igrejas

et al. 2023

Journal of Biomedical Informatics

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MetaTron: advancing biomedical annotation empowering relation annotation and collaboration

Irrera,

Marchesin,

Silvello

2024

BMC Bioinformatics

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Background The constant growth of biomedical data is accompanied by the need for new methodologies to effectively and efficiently extract machine-readable knowledge for training and testing purposes. A crucial aspect in this regard is creating large, often manually or semi-manually, annotated corpora vital for developing effective and efficient methods for tasks like relation extraction, topic recognition, and entity linking. However, manual annotation is expensive and time-consuming especially if not assisted by interactive, intuitive, and collaborative computer-aided tools. To support healthcare experts in the annotation process and foster annotated corpora creation, we present MetaTron. MetaTron is an open-source and free-to-use web-based annotation tool to annotate biomedical data interactively and collaboratively; it supports both mention-level and document-level annotations also integrating automatic built-in predictions. Moreover, MetaTron enables relation annotation with the support of ontologies, functionalities often overlooked by off-the-shelf annotation tools. Results We conducted a qualitative analysis to compare MetaTron with a set of manual annotation tools including TeamTat, INCEpTION, LightTag, MedTAG, and brat, on three sets of criteria: technical, data, and functional. A quantitative evaluation allowed us to assess MetaTron performances in terms of time and number of clicks to annotate a set of documents. The results indicated that MetaTron fulfills almost all the selected criteria and achieves the best performances. Conclusions MetaTron stands out as one of the few annotation tools targeting the biomedical domain supporting the annotation of relations, and fully customizable with documents in several formats—PDF included, as well as abstracts retrieved from PubMed, Semantic Scholar, and OpenAIRE. To meet any user need, we released MetaTron both as an online instance and as a Docker image locally deployable.

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Boosting biomedical document classification through the use of domain entity recognizers and semantic ontologies for document representation: The case of gluten bibliome

Cited by 3 publications

References 87 publications

MEDOBO: 18 Million+ MEDLINE Abstracts Tagged with OBO Ontologies

MEDOBO: 18 Million+ MEDLINE Abstracts Tagged with OBO Ontologies

A novel gluten knowledge base of potential biomedical and health-related interactions extracted from the literature: Using machine learning and graph analysis methodologies to reconstruct the bibliome

MetaTron: advancing biomedical annotation empowering relation annotation and collaboration

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