> top > projects

Projects

NameTDescription# Ann.AuthorMaintainerUpdated_atStatus

21-40 / 381 show all
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) 35EstelleChaix2018-01-13Released
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) 184EstelleChaix2018-01-13Released
Wangshuguang HZAU_bioinformatics_competition603wangshuguangwangshuguang2018-04-03Released
spacy-test Random set of articles used for testing in the development of the RESTful spaCy parsing web service. Since development is now finished, they are released for the community to use.137 KNico ColicNico Colic2019-03-16Released
RDoCTask2SampleData Each annotation file contains an annotated abstract with the most relevant sentence. The relevant sentence is annotated with the RDoC category name. The annotation data are formatted as json files. Please refer to the following page for a more detailed description of the json format http://www.pubannotation.org/docs/annotation-format/. 10mmanani1s2019-03-25Released
RDoCTask1SampleData Each annotation file contains an annotated abstract with an RDoC category. Each title span in these sample data is annotated with the corresponding related RDoC construct, although the RDoC category would apply for the entire abstract. The annotation data are formatted as json files. Please refer to the following page for a more detailed description of the json format http://www.pubannotation.org/docs/annotation-format/.20mmanani1s2019-03-25Released
craft-ca-core-dev Development data for CRAFT CA shared task, core concepts only. This project contains the development (training) annotations for the Concept Annotation task of the CRAFT Shared Task 2019. This particular set of concept annotations is the "core" set. See the task description for details, but this set contains only annotations to concepts that appear in the original 10 Open Biomedical Ontologies used for annotation. (That is to say, it does not contain any annotations to extension classes).62.3 KUniversity of Colorado Anschutz Medical Campuscraft-st2019-03-25Released
craft-ca-core-ex-dev Development data for CRAFT CA shared task, core concepts + EXTENSIONS. This project contains the development (training) annotations for the Concept Annotation task of the CRAFT Shared Task 2019. This particular set of concept annotations is the "core+extensions" set. See the task description for details, but this set contains annotations to concepts that appear in the original 10 Open Biomedical Ontologies used for annotation PLUS annotations to extension classes created using the core concepts.94.3 KUniversity of Colorado Anschutz Medical Campuscraft-st2019-03-25Released
craft-sa-dev Development data for CRAFT SA shared task. This project contains the development (training) annotations for the Structural Annotation task of the CRAFT Shared Task 2019. This particular set contains token and sentence annotations with tokens linked via dependency relations. These dependency relations were automatically generated using the manually curated CRAFT constituency treebank files as input.512 KUniversity of Colorado Anschutz Medical Campuscraft-st2019-03-25Released
123123123 123123123150yaoxinzhi2019-04-12Released
bionlp-st-ge-2016-test It is the benchmark test data set of the BioNLP-ST 2016 GE task. It includes Genia-style event annotations to 14 full paper articles which are about NFκB proteins. For testing purpose, however, annotations are all blinded, which means users cannot see the annotations in this project. Instead, annotations in any other project can be compared to the hidden annotations in this project, then the annotations in the project will be automatically evaluated based on the comparison. A participant of GE task can get the evaluation of his/her result of automatic annotation, through following process: Create a new project. Import documents from the project, bionlp-st-2016-test-proteins to your project. Import annotations from the project, bionlp-st-2016-test-proteins to your project. At this point, you may want to compare you project to this project, the benchmark data set. It will show that protein annotations in your project is 100% correct, but other annotations, e.g., events, are 0%. Produce event annotations, using your system, upon the protein annotations. Upload your event annotations to your project. Compare your project to this project, to get evaluation. GE 2016 benchmark data set is provided as multi-layer annotations which include: bionlp-st-ge-2016-reference: benchmark reference data set bionlp-st-ge-2016-test: benchmark test data set (this project) bionlp-st-ge-2016-test-proteins: protein annotation to the benchmark test data set Following is supporting resources: bionlp-st-ge-2016-coref: coreference annotation bionlp-st-ge-2016-uniprot: Protein annotation with UniProt IDs. pmc-enju-pas: dependency parsing result produced by Enju UBERON-AE: annotation for anatomical entities as defined in UBERON ICD10: annotation for disease names as defined in ICD10 GO-BP: annotation for biological process names as defined in GO GO-CC: annotation for cellular component names as defined in GO A SPARQL-driven search interface is provided at http://bionlp.dbcls.jp/sparql.7.99 KDBCLSJin-Dong Kim2019-04-30Released
GlyCosmos600-docs A random collection of 600 PubMed abstracts from 6 glycobiology-related journals: Glycobiology, Glycoconjugate journal, The Journal of biological chemistry, Journal of proteome research, Journal of proteomics, and Carbohydrate research. The whole PMIDs were collected on June 11, 2019. From each journal, 100 PMIDs were randomly sampled.0Jin-Dong Kim2019-06-11Released
AGAC_sample 874xiajingbo2019-06-30Released
AGAC_test 0xiajingbo2019-07-12Released
AGAC_training 3.32 Kxiajingbo2019-09-12Released
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 KChih-Hsuan Wei , Bethany R. Harris , Hung-Yu Kao and Zhiyong LuChih-Hsuan Wei2020-01-31Released
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 KLee et alHee-Jin Lee2020-02-01Released
AnEM_abstracts 250 documents selected randomly from citation abstracts Entity types: organism subdivision, anatomical system, organ, multi-tissue structure, tissue, cell, developing anatomical structure, cellular component, organism substance, immaterial anatomical entity and pathological formation Together with AnEM_full-texts, it is probably the largest manually annotated corpus on anatomical entities.1.95 KNaCTeMYue Wang2020-02-01Released
PIR-corpus1 The Protein Information Resource (PIR) is not biased towards any particular biomedical domain, and is expected to provide more diverse protein names in a given sample size. Annotation category: protein, compound-protein, acronym.4.44 KUniversity of Delaware and Georgetown University Medical CenterYue Wang2020-02-01Released
PennBioIE The PennBioIE corpus (0.9) covers two domains of biomedical knowledge. One is the inhibition of the cytochrome P450 family of enzymes (CYP450 or CYP for short) , and the other domain is the molecular genetics of dance (oncology or onco for short).23.9 KUPenn Biomedical Information Extraction ProjectYue Wang2020-02-01Released
NameT# Ann.AuthorMaintainerUpdated_atStatus

21-40 / 381 show all
bionlp-st-2016-SeeDev-training 35EstelleChaix2018-01-13Released
bionlp-st-2016-SeeDev-test 184EstelleChaix2018-01-13Released
Wangshuguang 603wangshuguangwangshuguang2018-04-03Released
spacy-test 137 KNico ColicNico Colic2019-03-16Released
RDoCTask2SampleData 10mmanani1s2019-03-25Released
RDoCTask1SampleData 20mmanani1s2019-03-25Released
craft-ca-core-dev 62.3 KUniversity of Colorado Anschutz Medical Campuscraft-st2019-03-25Released
craft-ca-core-ex-dev 94.3 KUniversity of Colorado Anschutz Medical Campuscraft-st2019-03-25Released
craft-sa-dev 512 KUniversity of Colorado Anschutz Medical Campuscraft-st2019-03-25Released
123123123 150yaoxinzhi2019-04-12Released
bionlp-st-ge-2016-test 7.99 KDBCLSJin-Dong Kim2019-04-30Released
GlyCosmos600-docs 0Jin-Dong Kim2019-06-11Released
AGAC_sample 874xiajingbo2019-06-30Released
AGAC_test 0xiajingbo2019-07-12Released
AGAC_training 3.32 Kxiajingbo2019-09-12Released
tmVarCorpus 1.43 KChih-Hsuan Wei , Bethany R. Harris , Hung-Yu Kao and Zhiyong LuChih-Hsuan Wei2020-01-31Released
CoMAGC 1.53 KLee et alHee-Jin Lee2020-02-01Released
AnEM_abstracts 1.95 KNaCTeMYue Wang2020-02-01Released
PIR-corpus1 4.44 KUniversity of Delaware and Georgetown University Medical CenterYue Wang2020-02-01Released
PennBioIE 23.9 KUPenn Biomedical Information Extraction ProjectYue Wang2020-02-01Released