A Silver Standard Corpus of Human Phenotype-Gene Relations
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Abstract (arXiv)
Human phenotype-gene relations are fundamental to fully understand the origin of some phenotypic abnormalities and their associated diseases. Biomedical literature is the most comprehensive source of these relations, however, we need Relation Extraction tools to automatically recognize them. Most of these tools require an annotated corpus and to the best of our knowledge, there is no corpus available annotated with human phenotype-gene relations. This paper presents the Phenotype-Gene Relations (PGR) corpus, a silver standard corpus of human phenotype and gene annotations and their relations. The corpus consists of 1712 abstracts, 5676 human phenotype annotations, 13835 gene annotations, and 4283 relations. We generated this corpus using Named-Entity Recognition tools, whose results were partially evaluated by eight curators, obtaining a precision of 87.01%. By using the corpus we were able to obtain promising results with two state-of-the-art deep learning tools, namely 78.05% of precision. The PGR corpus was made publicly available to the research community.
Key facts. Cited by 13 papers in the map (data through 7 Oct 2026). First posted on arXiv 26 Mar 2019.
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Citing papers per year: 2021: 2; 2022: 2; 2023: 5; 2024: 2; 2025: 1; 2026: 1.
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- 2023A Survey of Large Language Models in Medicine: Progress, Application, and Challengecited by 123
- 2023MultiDelete for Multimodal Machine Unlearningcited by 20
- 2024Large Language Model Benchmarks in Medical Taskscited by 16
- 2021Deep Neural Approaches to Relation Triplets Extraction: A Comprehensive Surveycited by 12
- 2022Generic and Trend-aware Curriculum Learning for Relation Extraction in Graph Neural Networkscited by 9
- 2021Deeper Clinical Document Understanding Using Relation Extractioncited by 7
- 2026Multimodal Unlearning Across Vision, Language, Video, and Audio: Survey of Methods, Datasets, and Benchmarkscited by 6
- 2022Supporting Medical Relation Extraction via Causality-Pruned Semantic Dependency Forestcited by 5
- 2023Complexity-Guided Curriculum Learning for Text Graphscited by 3
- 2023Curriculum Learning for Graph Neural Networks: A Multiview Competence-based Approachcited by 2
- 2024Taec: a Manually annotated text dataset for trait and phenotype extraction and entity linking in wheat breeding literaturecited by 1
- 2023Contrast with Major Classifier Vectors for Federated Medical Relation Extraction with Heterogeneous Label Distributioncited by 0
1 more
- 2026Multimodal Unlearning Across Vision, Language, Video, and Audio: Survey of Methods, Datasets, and Benchmarks8 Jul 2026
- 2025FairFlow: Mitigating Dataset Biases through Undecided Learning22 Mar 2025
- 2024Large Language Model Benchmarks in Medical Tasks28 Oct 2024
- 2024Taec: a Manually annotated text dataset for trait and phenotype extraction and entity linking in wheat breeding literature15 Jan 2024
- 2023Complexity-Guided Curriculum Learning for Text Graphs22 Nov 2023
- 2023MultiDelete for Multimodal Machine Unlearning18 Nov 2023
- 2023A Survey of Large Language Models in Medicine: Progress, Application, and Challenge9 Nov 2023
- 2023Curriculum Learning for Graph Neural Networks: A Multiview Competence-based Approach17 Jul 2023
- 2023Contrast with Major Classifier Vectors for Federated Medical Relation Extraction with Heterogeneous Label Distribution13 Jan 2023
- 2022Supporting Medical Relation Extraction via Causality-Pruned Semantic Dependency Forest29 Aug 2022
- 2022Generic and Trend-aware Curriculum Learning for Relation Extraction in Graph Neural Networks17 May 2022
- 2021Deeper Clinical Document Understanding Using Relation Extraction25 Dec 2021
1 more
- 2023A Survey of Large Language Models in Medicine: Progress, Application, and Challengecited by 123
- 2024Large Language Model Benchmarks in Medical Taskscited by 16
- 2021Deep Neural Approaches to Relation Triplets Extraction: A Comprehensive Surveycited by 12
- 2022Generic and Trend-aware Curriculum Learning for Relation Extraction in Graph Neural Networkscited by 9
- 2021Deeper Clinical Document Understanding Using Relation Extractioncited by 7
- 2022Supporting Medical Relation Extraction via Causality-Pruned Semantic Dependency Forestcited by 5
- 2023Complexity-Guided Curriculum Learning for Text Graphscited by 3
- 2023Curriculum Learning for Graph Neural Networks: A Multiview Competence-based Approachcited by 2
- 2024Taec: a Manually annotated text dataset for trait and phenotype extraction and entity linking in wheat breeding literaturecited by 1
- 2023Contrast with Major Classifier Vectors for Federated Medical Relation Extraction with Heterogeneous Label Distributioncited by 0
- 2025FairFlow: Mitigating Dataset Biases through Undecided Learningcited by 0
- 2025FairFlow: Mitigating Dataset Biases through Undecided Learning22 Mar 2025
- 2024Large Language Model Benchmarks in Medical Tasks28 Oct 2024
- 2024Taec: a Manually annotated text dataset for trait and phenotype extraction and entity linking in wheat breeding literature15 Jan 2024
- 2023Complexity-Guided Curriculum Learning for Text Graphs22 Nov 2023
- 2023A Survey of Large Language Models in Medicine: Progress, Application, and Challenge9 Nov 2023
- 2023Curriculum Learning for Graph Neural Networks: A Multiview Competence-based Approach17 Jul 2023
- 2023Contrast with Major Classifier Vectors for Federated Medical Relation Extraction with Heterogeneous Label Distribution13 Jan 2023
- 2022Supporting Medical Relation Extraction via Causality-Pruned Semantic Dependency Forest29 Aug 2022
- 2022Generic and Trend-aware Curriculum Learning for Relation Extraction in Graph Neural Networks17 May 2022
- 2021Deeper Clinical Document Understanding Using Relation Extraction25 Dec 2021
- 2021Deep Neural Approaches to Relation Triplets Extraction: A Comprehensive Survey31 Mar 2021