| RELASIGEBLAH7hhaider5 | | | 277 | | hhaider5 | 2023-11-29 | Developing | |
| pubmed_test | | | 0 | | | 2023-11-29 | | |
| Virus300 | | 300 abstracts from virology journals annotated with viral proteins and species | 0 | http://aclweb.org/anthology/W/W17/W17-2311.pdf | helencook | 2017-08-07 | Released | |
| proj_h_1 | | | 6.7 K | | | 2023-11-24 | | |
| 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 | |
| CoGe_Citation_Annotations | | Annotated PMC abstracts+full articles, that cite the "CoGe" papers (PMID: 18952863, 18269575).
Total Num Citations: 165
Total Num Unique Citations: 141
Total Num Abstracts: 165
Total Num Whole Articles: 165 | 0 | Heather Lent | hclent | 2023-11-29 | Uploading | |
| Staphylococcus | | | 7.46 K | haruo | haruo | 2023-11-29 | Testing | |
| test5 | | | 0 | | glennq | 2016-02-06 | | |
| EDAN70 | | NLP tagging of articles concerning covid19. | 0 | | fettmedknaoz | 2023-11-29 | | |
| 2015-BEL-Sample | | An attempt to upload 295 BEL statements, i.e. the sample set used for the 2015 BioCreative challenge.
| 58 | Fabio Rinaldi | Fabio Rinaldi | 2023-11-29 | Testing | |
| Oryza-OGER | | | 462 K | | fabiorinaldi | 2023-11-29 | | |
| EwhaLecture2020 | | testing | 0 | | | 2023-11-29 | Testing | |
| Genomics_Informatics | | Genomics & Informatics (NLM title abbreviation: Genomics Inform) is the official journal of the Korea Genome Organization.
Text corpus for this journal annotated with various levels of linguistic information would be a valuable resource as the process of information extraction requires syntactic, semantic, and higher levels of natural language processing. In this study, we publish our new corpus called GNI Corpus version 1.0, extracted and annotated from full texts of Genomics & Informatics, with NLTK (Natural Language ToolKit)-based text mining script. The preliminary version of the corpus could be used as a training and testing set of a system that serves a variety of functions for future biomedical text mining. | 35.3 K | Hyun-Seok Park | ewha-bio | 2023-11-29 | Beta | |
| testing | | testing | 0 | | ewha-bio | 2023-11-29 | Testing | |
| disease_ontology_term_microbe | | | 5 | | evangelos | 2023-11-29 | Developing | |
| SPECIES800 | | SPECIES 800 (S800): an abstract-based manually annotated corpus. S800 comprises 800 PubMed abstracts in which organism mentions were identified and mapped to the corresponding NCBI Taxonomy identifiers.
Described in:
The SPECIES and ORGANISMS Resources for Fast and Accurate Identification of Taxonomic Names in Text.
Pafilis E, Frankild SP, Fanini L, Faulwetter S, Pavloudi C, et al. (2013). PLoS ONE, 2013, 8(6): e65390. doi:10.1371/journal.pone.0065390 | 3.71 K | Evangelos Pafilis, Sune P. Frankild, Lucia Fanini, Sarah Faulwetter, Christina Pavloudi, Aikaterini Vasileiadou, Christos Arvanitidis, Lars Juhl Jensen | evangelos | 2023-11-28 | Released | |
| SPECIES800_autotagged | | This project comprises the SPECIES800 corpus documents automatically annotated by the Jensenlab tagger.
Annotated entity types are:
Genes/proteins from the mentioned organisms (and any human ones)
PubChem Compound identifiers
NCBI Taxonomy entries
Gene Ontology cellular component terms
BRENDA Tissue Ontology terms
Disease Ontology terms
Environment Ontology terms
The SPECIES 800 (S800) comprises 800 PubMed abstracts. In its original form species mentions were manually identified and mapped to the corresponding NCBI Taxonomy identifiers.
Described in:
The SPECIES and ORGANISMS Resources for Fast and Accurate Identification of Taxonomic Names in Text.
Pafilis E, Frankild SP, Fanini L, Faulwetter S, Pavloudi C, et al. (2013). PLoS ONE, 2013, 8(6): e65390. doi:10.1371/journal.pone.0065390.
The manually annotated corpus is also available as a PubAnnotation project (see here).
| 0 | Evangelos Pafilis, Sampo Pyysalo, Lars Juhl Jensen | evangelos | 2015-11-20 | Testing | |
| disease_gene_microbe_small | | Small version (48 abstract that mention both Crohns and S. aureus) for development purposes
Abbreviation: dgm Content: annotated abstracts on Crohn’s disease or on on Staphylococcus aureus (according to the jensenlab.org indexing resources) Entity types: (three for a start, organisms (NCBI Taxonomy taxa), disease (Disease Ontology terms), human genes (ENSEMBL proteins) Aim: Explore indirect associations of diseases to microbial species in this corpus via gene co-mentions | 536 | | evangelos | 2023-11-27 | Testing | |
| bionlp-st-2016-SeeDev-test | | Entities annotations from the test set of the BioNLP-ST 2016 SeeDev task.
SeeDev task focuses on seed storage and reserve accumulation on the model organism, Arabidopsis thaliana. The SeeDev task is based on the knowledge model Gene Regulation Network for Arabidopsis (GRNA) that meets the needs of text-mining (i.e. manual annotation of texts and automatic information extraction), experimental data indexing and retrieval and reuse in other plant systems. It is also expected to meet the requirements of the integration of the text knowledge with knowledge derived from experimental data in view of modeling in systems biology.
GRNA model defines 16 different types of entities, and 22 types of event (in five sets of event types) that may be combined in complex events.
For more information, please refer to the task website
All annotations :
Train set
Development set
Test set (without events)
| 184 | | EstelleChaix | 2023-11-29 | Released | |
| bionlp-st-2016-SeeDev-training | | Entities and event annotations from the training set of the BioNLP-ST 2016 SeeDev task.
SeeDev task focuses on seed storage and reserve accumulation on the model organism, Arabidopsis thaliana. The SeeDev task is based on the knowledge model Gene Regulation Network for Arabidopsis (GRNA) that meets the needs of text-mining (i.e. manual annotation of texts and automatic information extraction), experimental data indexing and retrieval and reuse in other plant systems. It is also expected to meet the requirements of the integration of the text knowledge with knowledge derived from experimental data in view of modeling in systems biology.
GRNA model defines 16 different types of entities, and 22 types of event (in five sets of event types) that may be combined in complex events.
For more information, please refer to the task website
All annotations :
Train set
Development set
Test set (without events)
| 35 | | EstelleChaix | 2023-11-28 | Released | |
