| CoMAGC | | In order to access the large amount of information in biomedical literature about genes implicated in various cancers both efficiently and accurately, the aid of text mining (TM) systems is invaluable. Current TM systems do target either gene-cancer relations or biological processes involving genes and cancers, but the former type produces information not comprehensive enough to explain how a gene affects a cancer, and the latter does not provide a concise summary of gene-cancer relations. In order to support the development of TM systems that are specifically targeting gene-cancer relations but are still able to capture complex information in biomedical sentences, we publish CoMAGC, a corpus with multi- faceted annotations of gene-cancer relations. In CoMAGC, a piece of annotation is composed of four semantically orthogonal concepts that together express 1) how a gene changes, 2) how a cancer changes and 3) the causality between the gene and the cancer. The multi-faceted annotations are shown to have high inter-annotator agreement. In addition, the annotations in CoMAGC allow us to infer the prospective roles of genes in cancers and to classify the genes into three classes according to the inferred roles. We encode the mapping between multi-faceted annotations and gene classes into 10 inference rules. The inference rules produce results with high accuracy as measured against human annotations. CoMAGC consists of 821 sentences on prostate, breast and ovarian cancers. Currently, the corpus deals with changes in gene expression levels among other types of gene changes. | 1.53 K | Lee et al | Hee-Jin Lee | 2023-11-24 | Released | |
| tmVarCorpus | | Wei C-H, Harris BR, Kao H-Y, Lu Z (2013) tmVar: A text mining approach for extracting sequence variants in biomedical literature, Bioinformatics, 29(11) 1433-1439, doi:10.1093/bioinformatics/btt156. | 1.43 K | Chih-Hsuan Wei , Bethany R. Harris , Hung-Yu Kao and Zhiyong Lu | Chih-Hsuan Wei | 2023-11-24 | Released | |
| Trait curation | | Project for trait curation in PGDBj | 479 | Sachiko Shirasawa | Sachiko Shirasawa | 2023-11-24 | Testing | |
| genia-medco-coref | | Coreference annotation made to the Genia corpus, following the MUC annotation scheme. It is a product of the collaboration between the Genia and the MedCo projects. | 45.9 K | MedCo project & Genia project | Jin-Dong Kim | 2023-11-24 | Developing | |
| bionlp-ost-19-BB-rel-ner-test | | | 125 | | ldeleger | 2023-11-24 | Developing | |
| LitCoin-training-merged | | | 14.8 K | | Jin-Dong Kim | 2023-11-24 | | |
| OryzaGP_2022 | | | 41.3 K | | larmande | 2023-11-24 | | |
| sonoma | | _ | 19.3 K | Standigm | | 2023-11-24 | Testing | |
| tees-test | | Random PMC document used for testing during the development of a RESTful TEES parsing web service. | 3.39 K | Nico Colic | Nico Colic | 2023-11-24 | Developing | |
| MENA-example2 | | | 3 | | Jin-Dong Kim | 2023-11-24 | Testing | |
| Test-Documents | | | 1 | | Jin-Dong Kim | 2023-11-24 | | |
| GlyCosmosP-Glycan-Motif | | | 8 | | Jin-Dong Kim | 2023-11-24 | Developing | |
| test10 | | | 212 | | Jin-Dong Kim | 2023-11-24 | | |
| PubMed_ArguminSci | | Predictions for PubMed automatically extracted with the ArguminSci tool (https://github.com/anlausch/ArguminSci). | 777 K | | zebet | 2023-11-24 | Released | |
| 0_colil | | | 781 K | | Yue Wang | 2023-11-24 | | |
| biomarkers | | IL/TNF biomarkers | 857 K | | alo33 | 2023-11-24 | | |
| PubCasesORDO | | ORDO annotation in PubCases | 865 K | | Toyofumi Fujiwara | 2023-11-24 | Beta | |
| Biotea | | NCBO annotation on full text for PMC articles. Currently including only a small set of 2811 articles corresponding to those supporting curated diesease-protein annotation from UniProt and with machine-processable full text. | 894 K | L. Garcia | | 2023-11-24 | Developing | |
| Epistemic_Statements | | The goal of this work is to identify epistemic statements in the scientific literature. An epistemic statement is a statement of unknowns, hypotheses, speculations, uncertainties, including statements of claims, hypotheses, questions, explanations, future opportunities, surprises, issues, or concerns within a sentence. The unit of an epistemic statement is a sentence automatically parsed. The classification is binary - epistemic statement or not. We will label epistemic statements only and one can assume that if a statement is not labeled, then it is not an epistemic statement.
The classifier is a CRF, trained on gold standard annotations of epistemic statements that are currently ongoing. We report an F-measure of 0.91 after 5-fold cross validation on a test set with 914 statements and an F-measure of 0.9 on a held out document with 130 statements. This project is still under development and is submitted to be used for the CovidLit project and associated Hackathon.
Please contact Mayla if you have any questions. | 1.42 M | | mboguslav | 2023-11-24 | Developing | |
| CORD-19-PD-UBERON | | PubDictionaries annotation for UBERON terms - updated at 2020-04-30
It is disease term annotation based on Uberon.
The terms in Uberon are uploaded in PubDictionaries
(Uberon), with which the annotations in this project are produced.
The parameter configuration used for this project is
here.
Note that it is an automatically generated dictionary-based annotation. It will be updated periodically, as the documents are increased, and the dictionary is improved. | 1.42 M | | Jin-Dong Kim | 2023-11-24 | Released | |
