{"url":"/dataset/pgr","name":"PGR","full_name":"Phenotype-Gene Relations","description_markdown":"Phenotype-Gene Relations (PGR) is a corpus that consists of 1712 abstracts, 5676 human phenotype annotations, 13835 gene annotations, and 4283 relations. \r\n\r\nSource: [A Silver Standard Corpus of Human Phenotype-Gene Relations](/paper/a-silver-standard-corpus-of-human-phenotype)","description_withheld":null,"homepage":"https://github.com/lasigeBioTM/PGR","introduced_date":null,"introduced_date_note":null,"introduced_by":{"paper":"/paper/a-silver-standard-corpus-of-human-phenotype","title":"A Silver Standard Corpus of Human Phenotype-Gene Relations","first_author":"Diana Sousa","url":null},"license":null,"modalities":[],"tasks":[{"name":"Named Entity Recognition (NER)","url":"/task/named-entity-recognition-ner","datasets_with_task":"/datasets/task/named-entity-recognition-ner"},{"name":"Relation Extraction","url":"/task/relation-extraction","datasets_with_task":"/datasets/task/relation-extraction"}],"languages":[],"variants":["PGR"],"data_loaders":[{"repo":"https://github.com/lasigeBioTM/PGR","url":"https://github.com/lasigeBioTM/PGR","frameworks":["jax"]}],"num_papers_in_archive":6,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/graph-regression-on-pgr","task":"Graph Regression","dataset_variant":"PGR","rows":9,"metrics":["R2","RMSE"],"first_row_in_archive_order":{"model":"ESA (Edge set attention, no positional encodings)","paper":"/paper/masked-attention-is-all-you-need-for-graphs","metrics":{"R2":"0.725±0.000","RMSE":"0.507±0.725"},"code_links":[{"title":"davidbuterez/edge-set-attention","url":"https://github.com/davidbuterez/edge-set-attention"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/relation-extraction-on-pgr","task":"Relation Extraction","dataset_variant":"PGR","rows":1,"metrics":["Macro F1"],"first_row_in_archive_order":{"model":"Spark NLP","paper":"/paper/deeper-clinical-document-understanding-using","metrics":{"Macro F1":"87.9"},"code_links":[{"title":"JohnSnowLabs/spark-nlp-workshop","url":"https://github.com/JohnSnowLabs/spark-nlp-workshop/blob/master/tutorials/Certification_Trainings/Healthcare/10.3.Clinical_RE_SparkNLP_Paper_Reproduce.ipynb"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/masked-attention-is-all-you-need-for-graphs","title":"An end-to-end attention-based approach for learning on graphs","date":"2024-02-16","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/pure-transformers-are-powerful-graph-learners","title":"Pure Transformers are Powerful Graph Learners","date":"2022-07-06","rows_on_this_dataset":1,"code_links":2,"syntology":{"read_at":"2026-09-25T09:33:49+00:00","samples_harvested":7,"samples_ran":7,"samples_unverified":0,"pointer_only_for_licence":7,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/deeper-clinical-document-understanding-using","title":"Deeper Clinical Document Understanding Using Relation Extraction","date":"2021-12-25","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/dropgnn-random-dropouts-increase-the","title":"DropGNN: Random Dropouts Increase the Expressiveness of Graph Neural Networks","date":"2021-11-11","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-25T09:33:49+00:00","samples_harvested":3,"samples_ran":2,"samples_unverified":1,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/do-transformers-really-perform-bad-for-graph","title":"Do Transformers Really Perform Bad for Graph Representation?","date":"2021-06-09","rows_on_this_dataset":1,"code_links":5,"syntology":null},{"paper":"/paper/how-attentive-are-graph-attention-networks","title":"How Attentive are Graph Attention Networks?","date":"2021-05-30","rows_on_this_dataset":1,"code_links":8,"syntology":{"read_at":"2026-09-25T09:33:49+00:00","samples_harvested":16,"samples_ran":11,"samples_unverified":5,"pointer_only_for_licence":1,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/principal-neighbourhood-aggregation-for-graph","title":"Principal Neighbourhood Aggregation for Graph Nets","date":"2020-04-12","rows_on_this_dataset":1,"code_links":8,"syntology":{"read_at":"2026-09-25T09:33:49+00:00","samples_harvested":55,"samples_ran":33,"samples_unverified":22,"pointer_only_for_licence":48,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/how-powerful-are-graph-neural-networks","title":"How Powerful are Graph Neural Networks?","date":"2018-10-01","rows_on_this_dataset":1,"code_links":19,"syntology":{"read_at":"2026-09-25T09:33:49+00:00","samples_harvested":10,"samples_ran":3,"samples_unverified":7,"pointer_only_for_licence":5,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/graph-attention-networks","title":"Graph Attention Networks","date":"2017-10-30","rows_on_this_dataset":1,"code_links":93,"syntology":{"read_at":"2026-09-25T09:33:49+00:00","samples_harvested":106,"samples_ran":51,"samples_unverified":55,"pointer_only_for_licence":43,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/semi-supervised-classification-with-graph","title":"Semi-Supervised Classification with Graph Convolutional Networks","date":"2016-09-09","rows_on_this_dataset":1,"code_links":55,"syntology":{"read_at":"2026-09-25T09:33:49+00:00","samples_harvested":58,"samples_ran":32,"samples_unverified":26,"pointer_only_for_licence":22,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}}],"syntology_totals":{"read_at":"2026-09-25T09:33:49+00:00","papers_with_samples":7,"samples_harvested":255,"samples_ran":139,"samples_unverified":116,"pointer_only_for_licence":126,"papers_with_no_sample_that_ran":0,"note":"the per-paper counts above, summed; not a rate"},"papers_note":"The archive never published its papers-using-dataset list; these are papers with a leaderboard row on this dataset's benchmarks."}