> top > projects

Projects

NameTDescription# Ann.AuthorMaintainer Updated_atStatus

81-100 / 556 show all
disease_ontology_term_microbe 5evangelos2023-11-29Developing
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). 0Evangelos Pafilis, Sampo Pyysalo, Lars Juhl Jensenevangelos2015-11-20Testing
testing testing0ewha-bio2023-11-29Testing
EwhaLecture2020 testing02023-11-29Testing
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 KHyun-Seok Parkewha-bio2023-11-29Beta
Oryza-OGER 462 Kfabiorinaldi2023-11-29
2015-BEL-Sample An attempt to upload 295 BEL statements, i.e. the sample set used for the 2015 BioCreative challenge. 58Fabio RinaldiFabio Rinaldi2023-11-29Testing
EDAN70 NLP tagging of articles concerning covid19.0fettmedknaoz2023-11-29
test5 0glennq2016-02-06
Staphylococcus 7.46 Kharuoharuo2023-11-29Testing
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 0Heather Lenthclent2023-11-29Uploading
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 Lee2023-11-24Released
proj_h_1 6.7 K2023-11-24
Virus300 300 abstracts from virology journals annotated with viral proteins and species0http://aclweb.org/anthology/W/W17/W17-2311.pdfhelencook2017-08-07Released
pubmed_test 02023-11-29
OGERtesthhaider5 465hhaider52023-11-29
RELASIGEBLAH7hhaider5 277hhaider52023-11-29Developing
Fragaria_ananassa_genes 0hidekih152023-11-28
ichiharatest_150825 test0ichihara_hisakoHisako Ichihara2023-11-29Testing
ichiharatest_150830_1 test99Hisako Ichihara2023-11-29Testing
NameT# Ann.AuthorMaintainer Updated_atStatus

81-100 / 556 show all
disease_ontology_term_microbe 5evangelos2023-11-29Developing
SPECIES800_autotagged 0Evangelos Pafilis, Sampo Pyysalo, Lars Juhl Jensenevangelos2015-11-20Testing
testing 0ewha-bio2023-11-29Testing
EwhaLecture2020 02023-11-29Testing
Genomics_Informatics 35.3 KHyun-Seok Parkewha-bio2023-11-29Beta
Oryza-OGER 462 Kfabiorinaldi2023-11-29
2015-BEL-Sample 58Fabio RinaldiFabio Rinaldi2023-11-29Testing
EDAN70 0fettmedknaoz2023-11-29
test5 0glennq2016-02-06
Staphylococcus 7.46 Kharuoharuo2023-11-29Testing
CoGe_Citation_Annotations 0Heather Lenthclent2023-11-29Uploading
CoMAGC 1.53 KLee et alHee-Jin Lee2023-11-24Released
proj_h_1 6.7 K2023-11-24
Virus300 0http://aclweb.org/anthology/W/W17/W17-2311.pdfhelencook2017-08-07Released
pubmed_test 02023-11-29
OGERtesthhaider5 465hhaider52023-11-29
RELASIGEBLAH7hhaider5 277hhaider52023-11-29Developing
Fragaria_ananassa_genes 0hidekih152023-11-28
ichiharatest_150825 0ichihara_hisakoHisako Ichihara2023-11-29Testing
ichiharatest_150830_1 99Hisako Ichihara2023-11-29Testing