| cancer_precision | | for gene mutaiton and cancer therapy | 8 | | serenity | 2023-11-29 | Testing | |
| 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)
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（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 K | | AikoHIRAKI | 2023-11-29 | Beta | |
| GlycoBiology-FMA | | FMA ontology-based annotation to GlycoBiology abstracts | 96.3 K | | Jin-Dong Kim | 2023-11-29 | Testing | |
| LitCoin-MeSH-Disease-2 | | Flase Negative全部入れてみた | 4.08 K | | yucca | 2023-11-29 | | |
| FA_PR25 | | FirstAuthor Protein Ontology 25 entries | 512 | | AikoHIRAKI | 2023-11-29 | Developing | |
| FirstAuthor10 | | FirstAuthor10 | 4.78 K | | AikoHIRAKI | 2023-11-29 | Developing | |
| Find2ER | | Find the Findings of Enzymatic Reaction | 0 | Akihiro Kameda | Akihiro Kameda | 2023-11-26 | 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 | |
| ENG_RE | | Entities and relations annotations from the following ontologies: Disease Ontology ('DO'), Gene Ontology ('GO'), Human Phenotype Ontology ('HPO'), and ChEBI ontology ('CHEBI'). | 224 | Diana Sousa | dpavot | 2023-11-29 | Developing | |
| 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 | |
| bionlp-st-2016-SeeDev-dev | | Entities and event annotations from the development 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)
| 61 | | EstelleChaix | 2023-11-29 | Released | |
| Gene_Chemical | | EMU abstract annotation | 0 | | zhoukaiyin | 2023-11-29 | Developing | |
| Grays_part1 | | Embryology | 1.44 K | | okubo | 2023-11-30 | Testing | |
| 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/.
| 10 | | mmanani1s | 2023-11-29 | Released | |
| 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/. | 20 | | mmanani1s | 2023-11-29 | Released | |
| c_corpus | | Documents included in the c_corpus: https://github.com/SMAFIRA/c_corpus/blob/master/SMAFIRAc_0.4_Annotations.csv | 107 K | | | 2023-11-29 | Released | |
| PMC-KEGG | | Documents from PMC including the word KEGG, with names of software tools and databases marked. | 27 | | yucca | 2023-11-28 | Developing | |
| DLUT931 | | DLUT NLP Lab.Test our event extration result for 16 GE task. | 4.57 K | | DLUT931 | 2023-11-30 | Testing | |
| DisGeNET | | Disease-Gene association annotation. | 3.12 M | Nuria Queralt | Jin-Dong Kim | 2023-11-24 | Beta | |
| Nanbyo-330-20171127 | | Disease descriptions extracted from MHLW | 19.8 K | | Toyofumi Fujiwara | 2023-11-26 | Testing | |
