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NameTDescription# Ann.AuthorMaintainerUpdated_atStatus

61-80 / 556 show all
CyanoBase Cyanobacteria are prokaryotic organisms that have served as important model organisms for studying oxygenic photosynthesis and have played a significant role in the Earthfs history as primary producers of atmospheric oxygen. Publication: http://www.aclweb.org/anthology/W12-24301.1 KKazusa DNA Research Institute and Database Center for Life Science (DBCLS)Yue Wang2023-11-26Released
jnlpba-st-training The training data used in the task came from the GENIA version 3.02 corpus, This was formed from a controlled search on MEDLINE using the MeSH terms "human", "blood cells" and "transcription factors". From this search, 1,999 abstracts were selected and hand annotated according to a small taxonomy of 48 classes based on a chemical classification. Among the classes, 36 terminal classes were used to annotate the GENIA corpus. For the shared task only the classes protein, DNA, RNA, cell line and cell type were used. The first three incorporate several subclasses from the original taxonomy while the last two are interesting in order to make the task realistic for post-processing by a potential template filling application. The publication year of the training set ranges over 1990~1999.51.1 KGENIAYue Wang2023-11-26Released
DisGeNET5_variant_disease The file contains variant-disease associations obtained by text mining MEDLINE abstracts using the BeFree system, including the variant and disease off sets. 144 KIBI GroupYue Wang2023-11-24Released
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
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 Wei2023-11-24Released
PubMed_ArguminSci Predictions for PubMed automatically extracted with the ArguminSci tool (https://github.com/anlausch/ArguminSci).777 Kzebet2023-11-24Released
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 MJin-Dong Kim2023-11-24Released
DisGeNET5_gene_disease The file contains gene-disease associations obtained by text mining MEDLINE abstracts using the BeFree system including the gene and disease off sets.2.04 MIBI GroupYue Wang2023-11-24Released
CORD-19_Custom_license_subset The Custom license subset of the CORD-19 dataset. The documents in this project will be updated as the CORD-19 dataset grows. See the COVID DATASET LICENSE AGREEMENT.5.08 MJin-Dong Kim2023-11-24Released
Inflammaging Inflammation axis23.4 Malo332023-11-24Released
geneset_names 0alo332022-04-26Released
Virus300 300 abstracts from virology journals annotated with viral proteins and species0http://aclweb.org/anthology/W/W17/W17-2311.pdfhelencook2017-08-07Released
GoldHamster 285 Kzebet2023-11-29Beta
Ab3P-abbreviations This corpus was developed during the creation of the Ab3P abbreviation definition identification tool. It includes 1250 manually annotated MEDLINE records. This gold standard includes 1221 abbreviation-definition pairs. Abbreviation definition identification based on automatic precision estimates Sunghwan Sohn, Donald C Comeau, Won Kim and W John Wilbur BMC Bioinformatics20089:402 DOI: 10.1186/1471-2105-9-4022.33 KSunghwan Sohn, Donald C Comeau, Won Kim and W John Wilburcomeau2023-11-29Beta
Age_blah 1.9 Kslee72682023-11-29Beta
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
FA_Top107-forWeb ※※※ !要データ加工! webリンク用には、この結果を加工して使っています。その他で使われる場合に、末尾記載の問題を別途解決する必要があります。 !要データ加工! ※※※ Top100+本来Top100に入るべきだった7レビューの計、107レビュー中99レビュー。 5414, 6076, 6930, 8403, 9643, 12112, 18544, 18829は、0denotationでドキュメント自体登録していません。 @AikoHIRAKIはtypoを修正したレビューフォルダ。 attributesの詳細はconfig参照。 ※※※ !注意! webリンク側のしばりで、選択文字列は複数のUniProtIDに対応していません。(例)Protein1~Protein7とある場合、 Protein1, 2, 3, 4, 5, 6, 7をさし、かつ全てにUniProtIDがあったとしても、1と7のみUniProtIDをとってきています。 "~"は、Protein2, 3, 4, 5, 6を意味していますが、positionではなく文字列で検索をかけているのと、見せ方の仕様上、これらのIDは全て未取得となっています。⇔GeneProteinでは"~"に2-6のIDsをもたせていました。 該当レビュー;14898(~=MAPK2, MAPK3, MAPK4, MAPK5, MAPK6), 10471(~=Ago2, Ago3) --------------------------------------- (例)ProteinAB...ProteinCD...ProteinB...ProteinDとある場合、 ProteinABは、ProteinAとBというLexical_Cueになっています。ProteinCDも同様に、ProteinCとD。BとDだけでは、このレビュー内ではProteinBやProteinDをさすことが分かるのですが、それ以外で使用する場合に、BとDにそれぞれ該当UniProtIDをあてるのは不適切です。 該当レビュー;11957(β4=itgb4, β1=itgb1, β5=itgb5, β3=itgb3) 他の例が出てきたら順次、ここに記載していきます。当座、これらは削除する必要があります。 attributeで削除フラグをつけるか、Jakeの機能がTextAEに実装されれば解決するか、検討して、何かしら分かるようにしておきます。 (例)ProteinA/B とある場合、 webリンクでは、"ProteinA"にUniProtID-Aを、"/B"にUniProtID-Bをつけています(リンク側のしばり)。webリンク以外で使われる場合には、別プロジェクトのFA_Top100Plus-GeneProteinで行っていたようなRelationを使って、"/B"ではなく、"ProteinB"として、UniProtID-Bと対応させる必要があります。現状のとり方ですと、要Relation箇所は救済出来ません。 Lexical cueには"/B"とありますが、Objectには"ProteinB"と残してあるので、Objectを参照して下さい。 但し、言語処理のようなpositionがご入用な場合には上では対応出来ていません。 該当レビュー;11935(/4=BMP4), 14898(/2=LATS2), 7412(/2=TSC2), 4629(/2=CtBP2) (webリンクでは、レビュー毎に完結しているので、"/B"がそのレビューで他の意味をなしていなければ対応出来るのと、文字列合致でリンクを貼っているためです。) !注意! ※※※ RelationのmergedはTextAEの既存機能で既に出来ます。10.3 KAikoHIRAKI2023-11-29Beta
LitCovid_Glycan-Motif-Structure PubDictionaries annotation for glycan-Motif terms.6.51 KISSAKU YAMADA2023-11-29Beta
LitCovid-sample-sentences 2.3 KJin-Dong Kim2023-11-29Beta
LitCovid-sample-PD-NCBITaxon 1.35 KJin-Dong Kim2023-11-29Beta
NameT# Ann.AuthorMaintainerUpdated_atStatus

61-80 / 556 show all
CyanoBase 1.1 KKazusa DNA Research Institute and Database Center for Life Science (DBCLS)Yue Wang2023-11-26Released
jnlpba-st-training 51.1 KGENIAYue Wang2023-11-26Released
DisGeNET5_variant_disease 144 KIBI GroupYue Wang2023-11-24Released
CoMAGC 1.53 KLee et alHee-Jin Lee2023-11-24Released
tmVarCorpus 1.43 KChih-Hsuan Wei , Bethany R. Harris , Hung-Yu Kao and Zhiyong LuChih-Hsuan Wei2023-11-24Released
PubMed_ArguminSci 777 Kzebet2023-11-24Released
CORD-19-PD-UBERON 1.42 MJin-Dong Kim2023-11-24Released
DisGeNET5_gene_disease 2.04 MIBI GroupYue Wang2023-11-24Released
CORD-19_Custom_license_subset 5.08 MJin-Dong Kim2023-11-24Released
Inflammaging 23.4 Malo332023-11-24Released
geneset_names 0alo332022-04-26Released
Virus300 0http://aclweb.org/anthology/W/W17/W17-2311.pdfhelencook2017-08-07Released
GoldHamster 285 Kzebet2023-11-29Beta
Ab3P-abbreviations 2.33 KSunghwan Sohn, Donald C Comeau, Won Kim and W John Wilburcomeau2023-11-29Beta
Age_blah 1.9 Kslee72682023-11-29Beta
Genomics_Informatics 35.3 KHyun-Seok Parkewha-bio2023-11-29Beta
FA_Top107-forWeb 10.3 KAikoHIRAKI2023-11-29Beta
LitCovid_Glycan-Motif-Structure 6.51 KISSAKU YAMADA2023-11-29Beta
LitCovid-sample-sentences 2.3 KJin-Dong Kim2023-11-29Beta
LitCovid-sample-PD-NCBITaxon 1.35 KJin-Dong Kim2023-11-29Beta