Genomics & informatics最新文献_第10页

Resources for assigning MeSH IDs to Japanese medical terms 用于将MeSH ID指定为日语医学术语的资源

Genomics & informatics Pub Date : 2019-06-01 DOI: 10.5808/GI.2019.17.2.e16

Yuka Tateisi

引用次数: 4

Improving spaCy dependency annotation and PoS tagging web service using independent NER services 使用独立的NER服务改进spaCy依赖性注释和PoS标记web服务

Genomics & informatics Pub Date : 2019-06-01 DOI: 10.5808/GI.2019.17.2.e21

N. Colic, Fabio Rinaldi

引用次数: 8

PharmacoNER Tagger: a deep learning-based tool for automatically finding chemicals and drugs in Spanish medical texts PharmacoNER Tagger:一个基于深度学习的工具，用于自动在西班牙医学文本中查找化学物质和药物

Genomics & informatics Pub Date : 2019-06-01 DOI: 10.5808/GI.2019.17.2.e15

Jordi Armengol-Estapé, Felipe Soares, M. Marimon, Martin Krallinger

{"title":"PharmacoNER Tagger: a deep learning-based tool for automatically finding chemicals and drugs in Spanish medical texts","authors":"Jordi Armengol-Estapé, Felipe Soares, M. Marimon, Martin Krallinger","doi":"10.5808/GI.2019.17.2.e15","DOIUrl":"https://doi.org/10.5808/GI.2019.17.2.e15","url":null,"abstract":"Automatically detecting mentions of pharmaceutical drugs and chemical substances is key for the subsequent extraction of relations of chemicals with other biomedical entities such as genes, proteins, diseases, adverse reactions or symptoms. The identification of drug mentions is also a prior step for complex event types such as drug dosage recognition, duration of medical treatments or drug repurposing. Formally, this task is known as named entity recognition (NER), meaning automatically identifying mentions of predefined entities of interest in running text. In the domain of medical texts, for chemical entity recognition (CER), techniques based on hand-crafted rules and graph-based models can provide adequate performance. In the recent years, the field of natural language processing has mainly pivoted to deep learning and state-of-the-art results for most tasks involving natural language are usually obtained with artificial neural networks. Competitive resources for drug name recognition in English medical texts are already available and heavily used, while for other languages such as Spanish these tools, although clearly needed were missing. In this work, we adapt an existing neural NER system, NeuroNER, to the particular domain of Spanish clinical case texts, and extend the neural network to be able to take into account additional features apart from the plain text. NeuroNER can be considered a competitive baseline system for Spanish drug and CER promoted by the Spanish national plan for the advancement of language technologies (Plan TL).","PeriodicalId":94288,"journal":{"name":"Genomics & informatics","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43537837","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 14

A review of drug knowledge discovery using BioNLP and tensor or matrix decomposition. 使用BioNLP和张量或矩阵分解的药物知识发现综述。

Genomics & informatics Pub Date : 2019-06-01 Epub Date: 2019-06-27 DOI: 10.5808/GI.2019.17.2.e18

Mina Gachloo, Yuxing Wang, Jingbo Xia

引用次数: 11

Improving the CONTES method for normalizing biomedical text entities with concepts from an ontology with (almost) no training data 改进CONTES方法，用(几乎)没有训练数据的本体概念规范化生物医学文本实体

Genomics & informatics Pub Date : 2019-06-01 DOI: 10.5808/GI.2019.17.2.e20

Arnaud Ferré, Mouhamadou Ba, Robert Bossy

{"title":"Improving the CONTES method for normalizing biomedical text entities with concepts from an ontology with (almost) no training data","authors":"Arnaud Ferré, Mouhamadou Ba, Robert Bossy","doi":"10.5808/GI.2019.17.2.e20","DOIUrl":"https://doi.org/10.5808/GI.2019.17.2.e20","url":null,"abstract":"Entity normalization, or entity linking in the general domain, is an information extraction task that aims to annotate/bind multiple words/expressions in raw text with semantic references, such as concepts of an ontology. An ontology consists minimally of a formally organized vocabulary or hierarchy of terms, which captures knowledge of a domain. Presently, machine-learning methods, often coupled with distributional representations, achieve good performance. However, these require large training datasets, which are not always available, especially for tasks in specialized domains. CONTES (CONcept-TErm System) is a supervised method that addresses entity normalization with ontology concepts using small training datasets. CONTES has some limitations, such as it does not scale well with very large ontologies, it tends to overgeneralize predictions, and it lacks valid representations for the out-of-vocabulary words. Here, we propose to assess different methods to reduce the dimensionality in the representation of the ontology. We also propose to calibrate parameters in order to make the predictions more accurate, and to address the problem of out-of-vocabulary words, with a specific method.","PeriodicalId":94288,"journal":{"name":"Genomics & informatics","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47317944","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 5

Introduction to BLAH5 special issue: recent progress on interoperability of biomedical text mining BLAH5特刊简介:生物医学文本挖掘互操作性的最新进展

Genomics & informatics Pub Date : 2019-06-01 DOI: 10.5808/GI.2019.17.2.e12

Jin-Dong Kim, K. Cohen, Nigel Collier, Zhiyong Lu, Fabio Rinaldi

引用次数: 0

OryzaGP: rice gene and protein dataset for named-entity recognition OryzaGP：用于命名实体识别的水稻基因和蛋白质数据集

Genomics & informatics Pub Date : 2019-06-01 DOI: 10.5808/GI.2019.17.2.e17

P. Larmande, Huy Do, Yue Wang

{"title":"OryzaGP: rice gene and protein dataset for named-entity recognition","authors":"P. Larmande, Huy Do, Yue Wang","doi":"10.5808/GI.2019.17.2.e17","DOIUrl":"https://doi.org/10.5808/GI.2019.17.2.e17","url":null,"abstract":"Text mining has become an important research method in biology, with its original purpose to extract biological entities, such as genes, proteins and phenotypic traits, to extend knowledge from scientific papers. However, few thorough studies on text mining and application development, for plant molecular biology data, have been performed, especially for rice, resulting in a lack of datasets available to solve named-entity recognition tasks for this species. Since there are rare benchmarks available for rice, we faced various difficulties in exploiting advanced machine learning methods for accurate analysis of the rice literature. To evaluate several approaches to automatically extract information from gene/protein entities, we built a new dataset for rice as a benchmark. This dataset is composed of a set of titles and abstracts, extracted from scientific papers focusing on the rice species, and is downloaded from PubMed. During the 5th Biomedical Linked Annotation Hackathon, a portion of the dataset was uploaded to PubAnnotation for sharing. Our ultimate goal is to offer a shared task of rice gene/protein name recognition through the BioNLP Open Shared Tasks framework using the dataset, to facilitate an open comparison and evaluation of different approaches to the task.","PeriodicalId":94288,"journal":{"name":"Genomics & informatics","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43083551","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Fully connecting the Observational Health Data Science and Informatics (OHDSI) initiative with the world of linked open data 将观察健康数据科学和信息学(OHDSI)计划与相关开放数据世界完全连接起来

Genomics & informatics Pub Date : 2019-06-01 DOI: 10.5808/GI.2019.17.2.e13

J. Banda

引用次数: 8

Biotea-2-Bioschemas, facilitating structured markup for semantically annotated scholarly publications Biotea-2-Bioschemas，为语义注释的学术出版物提供结构化标记

Genomics & informatics Pub Date : 2019-06-01 DOI: 10.5808/GI.2019.17.2.e14

L. García, Olga X. Giraldo, A. Garcia, D. Rebholz-Schuhmann

引用次数: 2