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ABNER specifications

Information


Unique identifier OMICS_01168
Name ABNER
Alternative name A Biomedical Named Entity Recognizer
Software type Package/Module
Interface Graphical user interface
Restrictions to use None
Operating system Unix/Linux, Mac OS, Windows
Computer skills Medium
Version 1.5
Stability Stable
Maintained Yes

Versioning


No version available

Maintainer


  • person_outline Burr Settles

Publication for A Biomedical Named Entity Recognizer

ABNER citations

 (73)
library_books

Deep learning meets ontologies: experiments to anchor the cardiovascular disease ontology in the biomedical literature

2018
J Biomed Semantics
PMCID: 5896136
PMID: 29650041
DOI: 10.1186/s13326-018-0181-1

[…] inological resources, e.g. resources generated from large databases like the UniProt Knowledgebase (UniProtKB) [].Machine Learning (ML) approaches such as conditional random fields [] that is used in ABNER [] and BANNER [].Lexicon based approaches that are based on large terminological resources, e.g. resources generated from large databases like the UniProt Knowledgebase (UniProtKB) [].Machine Le […]

call_split

Finding relevant biomedical datasets: the UC San Diego solution for the bioCADDIE Retrieval Challenge

2018
PMCID: 5861401
PMID: 29688374
DOI: 10.1093/database/bay017
call_split See protocol

[…] D-allwords’. We further developed a ‘PSD-keywords’ version that analysed only keywords extracted from Q. To identify valuable keywords from free-text questions, PSD-keywords firstly calls MetaMap (), a biomedical named entity recognizer, to identify the UMLS concepts from Q and then uses the UMLS concept set Q' as input to PSD, with the aim of eliminating the impact of less informative words in qu […]

library_books

MicroRNA 124 3p expression and its prospective functional pathways in hepatocellular carcinoma: A quantitative polymerase chain reaction, gene expression omnibus and bioinformatics study

2018
PMCID: 5840674
PMID: 29552191
DOI: 10.3892/ol.2018.8045

[…] 80/01/01’ (PDAT): ‘2015/05/25’ (PDAT)]. Subsequently, all pertinent molecules were retrieved and a list was generated, primarily comprising proteins and genes. Gene mention tagging was conducted with A Biomedical Named Entity Recognizer (ABNER; http://pages.cs.wisc.edu/~bsettles/abner/). ABNER also assisted in conjunction resolution. Gene name normalization conformed to the standard names in the E […]

library_books

Analysis of microarrays of miR 34a and its identification of prospective target gene signature in hepatocellular carcinoma

2018
BMC Cancer
PMCID: 5753510
PMID: 29298665
DOI: 10.1186/s12885-017-3941-x

[…] vacizumab) and (“1980/01/01” [PDAT]: “2015/05/25” [PDAT]). All genes and proteins related to the key words were extracted and gathered in a list with the following of gene mention tagging by applying A Biomedical Named Entity Recognizer (ABNER, an open source tool for automatically tagging genes, proteins and other entity names in text, http://pages.cs.wisc.edu/bsettles/abner/) [] and conjunction […]

library_books

Potential role of microRNA 223 3p in the tumorigenesis of hepatocellular carcinoma: A comprehensive study based on data mining and bioinformatics

2017
PMCID: 5783470
PMID: 29207133
DOI: 10.3892/mmr.2017.8167

[…] ly in HCC in studies published between 01/01/1980 and 05/25/2015 were gathered with NLP techniques, as described in our previous study (). Additionally, the extracted genes were added to a list using ABNER (http://pages.cs.wisc.edu/~bsettles/abner/) (). The correlations between hub genes and miR-223-3p were assessed using Spearman's correlation. […]

library_books

Semantic annotation in biomedicine: the current landscape

2017
J Biomed Semantics
PMCID: 5610427
PMID: 28938912
DOI: 10.1186/s13326-017-0153-x

[…] based on machine learning (ML) models, more specifically Hidden Markov Models (HMM), Conditional Random Fields (CRF), and Support Vector Machines (SVM), have become more prominent in the recent years.ABNER [] was one of the earlier works that benefited from CRF models and was trained for five specific entity types, namely Protein, DNA, RNA, Cell Line, and Cell Type. ABNER extracted features based […]

Citations

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ABNER institution(s)
Department of Computer Sciences and Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison Madison, WI, USA

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