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   Articles with "biomedical relation" as a keyword



Neural network-based approaches for biomedical relation classification: A review

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recommendations! Published in 2019 at "Journal of biomedical informatics"

DOI: 10.1016/j.jbi.2019.103294

Abstract: The explosive growth of biomedical literature has created a rich source of knowledge, such as that on protein-protein interactions (PPIs) and drug-drug interactions (DDIs), locked in unstructured free text. Biomedical relation classification aims to automatically… read more here.

Keywords: network based; network; biomedical relation; based approaches ... See more keywords

BioGSF: a graph-driven semantic feature integration framework for biomedical relation extraction

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recommendations! Published in 2024 at "Briefings in Bioinformatics"

DOI: 10.1093/bib/bbaf025

Abstract: Abstract The automatic and accurate extraction of diverse biomedical relations from literature constitutes the core elements of medical knowledge graphs, which are indispensable for healthcare artificial intelligence. Currently, fine-tuning through stacking various neural networks on… read more here.

Keywords: framework; biomedical relation; extraction; relation extraction ... See more keywords

K-RET: Knowledgeable Biomedical Relation Extraction System.

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recommendations! Published in 2023 at "Bioinformatics"

DOI: 10.1093/bioinformatics/btad174

Abstract: MOTIVATION Relation Extraction (RE) is a crucial process to deal with the amount of text published daily, for example, to find missing associations in a database. RE is a text mining task for which the… read more here.

Keywords: relation extraction; knowledgeable biomedical; biomedical relation; extraction system ... See more keywords

Document-level biomedical relation extraction via hierarchical tree graph and relation segmentation module

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recommendations! Published in 2024 at "Bioinformatics"

DOI: 10.1093/bioinformatics/btae418

Abstract: Abstract Motivation Biomedical relation extraction at the document level (Bio-DocRE) involves extracting relation instances from biomedical texts that span multiple sentences, often containing various entity concepts such as genes, diseases, chemicals, variants, etc. Currently, this… read more here.

Keywords: relation; segmentation; biomedical relation; document level ... See more keywords

Enhancing biomedical relation extraction with directionality

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recommendations! Published in 2025 at "Bioinformatics"

DOI: 10.1093/bioinformatics/btaf226

Abstract: Abstract Summary Biological relation networks contain rich information for understanding the biological mechanisms behind the relationship of entities such as genes, proteins, diseases, and chemicals. The vast growth of biomedical literature poses significant challenges in… read more here.

Keywords: directionality; relation; biomedical relation; language ... See more keywords

Biomedical Relation Extraction With Knowledge Graph-Based Recommendations

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recommendations! Published in 2022 at "IEEE Journal of Biomedical and Health Informatics"

DOI: 10.1109/jbhi.2022.3173558

Abstract: Biomedical Relation Extraction (RE) systems identify and classify relations between biomedical entities to enhance our knowledge of biological and medical processes. Most state-of-the-art systems use deep learning approaches, mainly to target relations between entities of… read more here.

Keywords: system; knowledge graph; relation extraction; biomedical relation ... See more keywords

Distantly Supervised Biomedical Relation Extraction via Negative Learning and Noisy Student Self-Training

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recommendations! Published in 2024 at "IEEE/ACM Transactions on Computational Biology and Bioinformatics"

DOI: 10.1109/tcbb.2024.3412174

Abstract: Biomedical relation extraction aims to identify underlying relationships among entities, such as gene associations and drug interactions, within biomedical texts. Despite advancements in relation extraction in general knowledge domains, the scarcity of labeled training data… read more here.

Keywords: relation; noisy student; biomedical relation; negative learning ... See more keywords

Leveraging shortest dependency paths in low-resource biomedical relation extraction

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recommendations! Published in 2024 at "BMC Medical Informatics and Decision Making"

DOI: 10.1186/s12911-024-02592-2

Abstract: Biomedical Relation Extraction (RE) is essential for uncovering complex relationships between biomedical entities within text. However, training RE classifiers is challenging in low-resource biomedical applications with few labeled examples. We explore the potential of Shortest… read more here.

Keywords: biomedical relation; resource biomedical; shortest dependency; dependency paths ... See more keywords