| 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 K | Lee et al | Hee-Jin Lee | 2023-11-24 | Released | |
| parkinson | | parkinson's disease | 1.55 K | Kyungeun | Kyungeun | 2023-11-29 | Testing | |
| PMA_age_indications | | | 1.57 K | | therightstef | 2023-11-29 | Developing | |
| consensus_PMA_Age_Indications | | | 1.7 K | | laurenc | 2023-11-28 | Beta | |
| GlyCosmos600-CLO | | | 1.73 K | | Jin-Dong Kim | 2023-11-28 | Testing | |
| bionlp-ost-19-BB-kb-dev | | | 1.89 K | | ldeleger | 2023-11-26 | Developing | |
| bionlp-ost-19-BB-kb-ner-dev | | | 1.9 K | | ldeleger | 2023-11-29 | Developing | |
| Age_blah | | | 1.9 K | | slee7268 | 2023-11-29 | Beta | |
| 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. | 1.91 K | NaCTeM | Yue Wang | 2023-11-29 | Released | |
| updated_tagging_age_PMA_annotations | | | 1.94 K | | laurenc | 2023-11-26 | Developing | |
| bionlp-ost-19-BB-rel-dev | | | 1.97 K | | ldeleger | 2023-11-29 | Developing | |
| bionlp-ost-19-BB-rel-ner-dev | | | 1.98 K | | ldeleger | 2023-11-29 | Developing | |
| bionlp-ost-19-BB-kb-test | | | 2.04 K | | ldeleger | 2023-11-29 | Developing | |
| bionlp-ost-19-BB-norm-test | | | 2.05 K | | ldeleger | 2023-11-28 | Developing | |
| bionlp-ost-19-BB-rel-test | | | 2.07 K | | ldeleger | 2023-11-29 | Developing | |
| FA_Top100-Disease | | 1/2 FirstAuthor Top100 (201811-201910) for diseases
MONDO & HPO | 2.14 K | | AikoHIRAKI | 2023-11-29 | | |
| LocText | | The manually annotated corpus consists of 100 PubMed abstracts annotated for proteins, subcellular localizations, organisms and relations between them. The focus of the corpus is on annotation of proteins and their subcellular localizations. | 2.29 K | Goldberg et al | Shrikant Vinchurkar | 2023-11-29 | Released | |
| bionlp-ost-19-SeeDev-bin-test | | | 2.32 K | | ldeleger | 2023-11-28 | Developing | |
| 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: 10.1186/1471-2105-9-402 | 2.33 K | Sunghwan Sohn, Donald C Comeau, Won Kim and W John Wilbur | comeau | 2023-11-29 | Beta | |
| GGDB-2020 | | | 2.44 K | | angata | 2023-11-30 | Developing | |
