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Projects (159)

Name Description # Ann. Author Maintainer updated at Status
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. <br> Described in: <a href="http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0065390" target="blank">The SPECIES and ORGANISMS Resources for Fast and Accurate Identification of Taxonomic Names in Text.</a> Pafilis E, Frankild SP, Fanini L, Faulwetter S, Pavloudi C, et al. (2013). PLoS ONE, 2013, 8(6): e65390. <a href="http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0065390" target="blank">doi:10.1371/journal.pone.0065390</a> 3,708 Evangelos Pafilis, Sune P. Frankild, Lucia Fanini, Sarah Faulwetter, Christina Pavloudi, Aikaterini Vasileiadou, Christos Arvanitidis, Lars Juhl Jensen evangelos 2015-11-20 Released
AnEM_abstracts 250 documents selected randomly from citation abstracts <br> 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<br> Together with <a href="http://pubannotation.org/projects/AnEM_full-texts">AnEM_full-texts</a>, it is probably the largest manually annotated corpus on anatomical entities. 1,946 NaCTeM Yue Wang 2016-06-07 Released
NCBIDiseaseCorpus The NCBI disease corpus is fully annotated at the mention and concept level to serve as a research resource for the biomedical natural language processing community. 6,881 Rezarta Islamaj Doğan,Robert Leaman,Zhiyong Lu Chih-Hsuan Wei 2015-08-06 Released
bionlp-st-ge-2016-test-tees NER and event extraction produced by TEES (with the default GE11 model) for the 14 full papers used in the BioNLP 2016 GE task test corpus. 9,171 Nico Colic Nico Colic 2016-05-25 Released
bionlp-st-ge-2016-test-proteins <p>Protein annotations to the benchmark test data set of the BioNLP-ST 2016 GE task. A participant of the GE task may import the documents and annotations of this project to his/her own project, to begin with producing event annotations.</p> <p>For more details, please refer to the benchmark test data set (<a href="http://pubannotation.org/projects/bionlp-st-ge-2016-test">bionlp-st-ge-2016-test</a>)</li>.</p> 4,338 DBCLS Jin-Dong Kim 2016-05-04 Released
bionlp-st-id-2011-training The training dataset from the infectious diseases (ID) task in the BioNLP Shared Task 2011. <br> Entity types: <br>- Genes and gene products: gene, RNA, and protein name mentions. <br>- Two-component systems: mentions of the names of two-component regulatory systems, frequently embedding the names of the two Proteins forming the system.<br>- Chemicals: mentions of chemical compounds such as "NaCL".<br>- Organisms: mentions of organism names or organism specification through specific properties (e.g. "graRS mutant").<br>- Regulons/Operons: mentions of names of specific regulons and operons. 5,609 University of Tokyo Tsujii Laboratory, NaCTeM and Biocomplexity Institute of Virginia Tech Yue Wang 2017-04-18 Released
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. 1,528 Lee et al Hee-Jin Lee 2015-02-24 Released
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). 23,881 UPenn Biomedical Information Extraction Project Yue Wang 2016-12-06 Released
bionlp-st-ge-2016-uniprot <p>UniProt protein annotation to the benchmark data set of BioNLP-ST 2016 GE task: reference data set (<a href="http://pubannotation.org/projects/bionlp-st-ge-2016-reference">bionlp-st-ge-2016-reference</a>) and test data set (<a href="http://pubannotation.org/projects/bionlp-st-ge-2016-test">bionlp-st-ge-2016-test</a>).</p> <p>The annotations are produced based on a <a href="http://pubdictionaries.org/dictionaries/nfkb-rel-proteins">dictionary</a> which is semi-automatically compiled for the 34 full paper articles included in the benchmark data set (20 in the reference data set + 14 in the test data set).</p> <p>For detailed information about BioNLP-ST GE 2016 task data sets, please refer to the benchmark reference data set (<a href="http://pubannotation.org/projects/bionlp-st-ge-2016-reference">bionlp-st-ge-2016-reference</a>) and benchmark test data set (<a href="http://pubannotation.org/projects/bionlp-st-ge-2016-test">bionlp-st-ge-2016-test</a>).</p> 16,198 DBCLS Jin-Dong Kim 2016-05-22 Beta
DisGeNET Disease-Gene association annotation. 3,117,504 Nuria Queralt Jin-Dong Kim 2016-01-28 Beta