Dataset Information

CoMAGC: a corpus with multi-faceted annotations of gene-cancer relations.

ABSTRACT: BACKGROUND: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. RESULTS:In this paper, we present a corpus for the development of TM systems that are specifically targeting gene-cancer relations but are still able to capture complex information in biomedical sentences. We describe 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, we show that 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, we deal with changes in gene expression levels among other types of gene changes. The corpus is available at http://biopathway.org/CoMAGCunder the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/2.0). CONCLUSIONS:The corpus will be an important resource for the development of advanced TM systems on gene-cancer relations.

SUBMITTER: Lee HJ

PROVIDER: S-EPMC3833657 | biostudies-literature | 2013 Nov

REPOSITORIES: biostudies-literature

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CoMAGC: a corpus with multi-faceted annotations of gene-cancer relations.

Lee Hee-Jin HJ Shim Sang-Hyung SH Song Mi-Ryoung MR Lee Hyunju H Park Jong C JC

BMC bioinformatics 20131114

<h4>Background</h4>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-canc ...[more]

PMID: 24225062

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Project description:Background and aimsThe new coronavirus disease (COVID-19) is a systemic disease. Mounting evidence depict signs and symptoms involving multiple organs, most of which supported by pathological data. A plausible link to these manifestations is vascular and endothelial dysfunction/damage. However, much of the current knowledge relies on opinion and incipient evidence. We aim to objectively appraise current evidence on the association between COVID-19 and vascular disease, specifically endotheliitis and vasculitis.MethodsTwo researchers independently entered the search terms COVID-19 OR SARS-CoV-2 AND vasculitis, endotheliitis OR endothelium in the following online platforms: MedRxiv and LitCovid (PubMed). The search period was set from November 1, 2019 to August 28, 2020. Manuscripts with unavailable full texts, not in English, mainly on pre-clinical data, presenting only study designs or not directly related to the topics of this review were excluded. Retrospective and prospective studies, especially longitudinal ones, were given priority to the purpose of this review. Since there was paucity of prospective controlled evidence, case reports/series were also considered.ResultsA total of 318 manuscripts were initially found. Sixty-seven (21%) were excluded: 59 (18.5%) met exclusion criteria and 8 (2.5%) were duplicates. One hundred and forty-two manuscripts (44,6%) did not provide original data and were also excluded: 35 (11%) were comments, 108 (33.9%) reviews; 1 (0.3%) position paper. One hundred and seven (33.6%) studies were considered for the present scoping review: 81 (25,5%) case reports/series; 18 (5.7%) prospective; 8 (2.5%) retrospective. Viral inclusions in endothelial cells, mononuclear cell infiltrates in the intima of small vessels and markers of endothelial cell apoptosis were demonstrated. Specificities of COVID-19 may lead to diverse vascular manifestations in different levels of the vascular bed.ConclusionsEvidence indicates that COVID-19 targets vasculature and endothelium. However, high quality data is still lacking and studies with prospective designs and appropriately matched controls are needed.

Project description:BackgroundMany new medicines have been derived from natural sources such as plants, which have a long history of being used for disease treatment. Thus, their benefits and side effects have been studied, and plant-related information including plant and disease relations have been accumulated in Medline articles. Because numerous articles are available in Medline and are written in natural language, text-mining is important. However, a corpus of plant and disease relations is not available yet. Thus, we aimed to construct such a corpus.Methods and resultsIn this study, we designed and annotated a plant-disease relations corpus, and proposed a computational model to predict plant-disease relations using the corpus. We categorized plant and disease relations into four types: treatments of diseases, causes of diseases, associations, and negative relations. To construct a corpus of plant-disease relations, we first created its annotation guidelines and randomly selected 200 Medline abstracts. From these abstracts, we identified 1,405 and 1,755 plant and disease mentions, annotated to 105 and 237 unique plant and disease identifiers, respectively. When we selected sentences containing at least one plant and one disease mention, we extracted 878 plant and 1,077 disease entities, which finally generated a corpus of plant-disease relations including 1,309 relations from 199 abstracts. To verify the effectiveness of the corpus, we proposed a convolutional neural network model with the shortest dependency path (SDP-CNN) and applied it to the constructed corpus. The micro F-score with ten-fold cross-validation was found to be 0.764. We also applied the proposed SDP-CNN model to all Medline abstracts. When we measured its performance for 483 randomly selected plant-disease co-occurring sentences, the model showed a precision of 0.707.ConclusionThe plant-disease relations corpus is unique and represents an important resource for biomedical text-mining. The corpus of plant and disease relations is available at http://gcancer.org/pdr/.

Dataset Information

CoMAGC: a corpus with multi-faceted annotations of gene-cancer relations.

Publications

CoMAGC: a corpus with multi-faceted annotations of gene-cancer relations.

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