Literature DB >> 23736528

BeCAS: biomedical concept recognition services and visualization.

Tiago Nunes1, David Campos, Sérgio Matos, José Luís Oliveira.   

Abstract

SUMMARY: The continuous growth of the biomedical scientific literature has been motivating the development of text-mining tools able to efficiently process all this information. Although numerous domain-specific solutions are available, there is no web-based concept-recognition system that combines the ability to select multiple concept types to annotate, to reference external databases and to automatically annotate nested and intercepted concepts. BeCAS, the Biomedical Concept Annotation System, is an API for biomedical concept identification and a web-based tool that addresses these limitations. MEDLINE abstracts or free text can be annotated directly in the web interface, where identified concepts are enriched with links to reference databases. Using its customizable widget, it can also be used to augment external web pages with concept highlighting features. Furthermore, all text-processing and annotation features are made available through an HTTP REST API, allowing integration in any text-processing pipeline. AVAILABILITY: BeCAS is freely available for non-commercial use at http://bioinformatics.ua.pt/becas. CONTACTS: tiago.nunes@ua.pt or jlo@ua.pt.

Mesh:

Year:  2013        PMID: 23736528     DOI: 10.1093/bioinformatics/btt317

Source DB:  PubMed          Journal:  Bioinformatics        ISSN: 1367-4803            Impact factor:   6.937


  33 in total

1.  Beyond accuracy: creating interoperable and scalable text-mining web services.

Authors:  Chih-Hsuan Wei; Robert Leaman; Zhiyong Lu
Journal:  Bioinformatics       Date:  2016-02-16       Impact factor: 6.937

2.  Towards an Obesity-Cancer Knowledge Base: Biomedical Entity Identification and Relation Detection.

Authors:  Juan Antonio Lossio-Ventura; William Hogan; François Modave; Amanda Hicks; Josh Hanna; Yi Guo; Zhe He; Jiang Bian
Journal:  Proceedings (IEEE Int Conf Bioinformatics Biomed)       Date:  2017-01-19

3.  An effective biomedical document classification scheme in support of biocuration: addressing class imbalance.

Authors:  Xiangying Jiang; Martin Ringwald; Judith A Blake; Cecilia Arighi; Gongbo Zhang; Hagit Shatkay
Journal:  Database (Oxford)       Date:  2019-01-01       Impact factor: 3.451

4.  Leveraging word embeddings and medical entity extraction for biomedical dataset retrieval using unstructured texts.

Authors:  Yanshan Wang; Majid Rastegar-Mojarad; Ravikumar Komandur-Elayavilli; Hongfang Liu
Journal:  Database (Oxford)       Date:  2017-01-01       Impact factor: 3.451

5.  Integrating image caption information into biomedical document classification in support of biocuration.

Authors:  Xiangying Jiang; Pengyuan Li; James Kadin; Judith A Blake; Martin Ringwald; Hagit Shatkay
Journal:  Database (Oxford)       Date:  2020-01-01       Impact factor: 3.451

6.  Extraction of chemical-protein interactions from the literature using neural networks and narrow instance representation.

Authors:  Rui Antunes; Sérgio Matos
Journal:  Database (Oxford)       Date:  2019-01-01       Impact factor: 3.451

7.  Assessing the impact of case sensitivity and term information gain on biomedical concept recognition.

Authors:  Tudor Groza; Karin Verspoor
Journal:  PLoS One       Date:  2015-03-19       Impact factor: 3.240

8.  A document processing pipeline for annotating chemical entities in scientific documents.

Authors:  David Campos; Sérgio Matos; José L Oliveira
Journal:  J Cheminform       Date:  2015-01-19       Impact factor: 5.514

9.  Generation of silver standard concept annotations from biomedical texts with special relevance to phenotypes.

Authors:  Anika Oellrich; Nigel Collier; Damian Smedley; Tudor Groza
Journal:  PLoS One       Date:  2015-01-21       Impact factor: 3.240

10.  Concept selection for phenotypes and diseases using learn to rank.

Authors:  Nigel Collier; Anika Oellrich; Tudor Groza
Journal:  J Biomed Semantics       Date:  2015-06-01
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