Automatic extraction of nanoparticle properties using natural language processing: NanoSifter an application to acquire PAMAM dendrimer properties.
In this study, we demonstrate the use of natural language processing methods to extract, from nanomedicine literature, numeric values of biomedical property terms of poly(amidoamine) dendrimers. We have developed a method for extracting these values for properties taken from the NanoParticle Ontolog...
Main Authors: | , , , |
---|---|
Format: | Article |
Language: | English |
Published: |
Public Library of Science (PLoS)
2014-01-01
|
Series: | PLoS ONE |
Online Access: | http://europepmc.org/articles/PMC3879259?pdf=render |
id |
doaj-e9e3699546a14beb9d57625e18f6960b |
---|---|
record_format |
Article |
spelling |
doaj-e9e3699546a14beb9d57625e18f6960b2020-11-25T01:55:54ZengPublic Library of Science (PLoS)PLoS ONE1932-62032014-01-0191e8393210.1371/journal.pone.0083932Automatic extraction of nanoparticle properties using natural language processing: NanoSifter an application to acquire PAMAM dendrimer properties.David E JonesSean IgoJohn HurdleJulio C FacelliIn this study, we demonstrate the use of natural language processing methods to extract, from nanomedicine literature, numeric values of biomedical property terms of poly(amidoamine) dendrimers. We have developed a method for extracting these values for properties taken from the NanoParticle Ontology, using the General Architecture for Text Engineering and a Nearly-New Information Extraction System. We also created a method for associating the identified numeric values with their corresponding dendrimer properties, called NanoSifter. We demonstrate that our system can correctly extract numeric values of dendrimer properties reported in the cancer treatment literature with high recall, precision, and f-measure. The micro-averaged recall was 0.99, precision was 0.84, and f-measure was 0.91. Similarly, the macro-averaged recall was 0.99, precision was 0.87, and f-measure was 0.92. To our knowledge, these results are the first application of text mining to extract and associate dendrimer property terms and their corresponding numeric values.http://europepmc.org/articles/PMC3879259?pdf=render |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
David E Jones Sean Igo John Hurdle Julio C Facelli |
spellingShingle |
David E Jones Sean Igo John Hurdle Julio C Facelli Automatic extraction of nanoparticle properties using natural language processing: NanoSifter an application to acquire PAMAM dendrimer properties. PLoS ONE |
author_facet |
David E Jones Sean Igo John Hurdle Julio C Facelli |
author_sort |
David E Jones |
title |
Automatic extraction of nanoparticle properties using natural language processing: NanoSifter an application to acquire PAMAM dendrimer properties. |
title_short |
Automatic extraction of nanoparticle properties using natural language processing: NanoSifter an application to acquire PAMAM dendrimer properties. |
title_full |
Automatic extraction of nanoparticle properties using natural language processing: NanoSifter an application to acquire PAMAM dendrimer properties. |
title_fullStr |
Automatic extraction of nanoparticle properties using natural language processing: NanoSifter an application to acquire PAMAM dendrimer properties. |
title_full_unstemmed |
Automatic extraction of nanoparticle properties using natural language processing: NanoSifter an application to acquire PAMAM dendrimer properties. |
title_sort |
automatic extraction of nanoparticle properties using natural language processing: nanosifter an application to acquire pamam dendrimer properties. |
publisher |
Public Library of Science (PLoS) |
series |
PLoS ONE |
issn |
1932-6203 |
publishDate |
2014-01-01 |
description |
In this study, we demonstrate the use of natural language processing methods to extract, from nanomedicine literature, numeric values of biomedical property terms of poly(amidoamine) dendrimers. We have developed a method for extracting these values for properties taken from the NanoParticle Ontology, using the General Architecture for Text Engineering and a Nearly-New Information Extraction System. We also created a method for associating the identified numeric values with their corresponding dendrimer properties, called NanoSifter. We demonstrate that our system can correctly extract numeric values of dendrimer properties reported in the cancer treatment literature with high recall, precision, and f-measure. The micro-averaged recall was 0.99, precision was 0.84, and f-measure was 0.91. Similarly, the macro-averaged recall was 0.99, precision was 0.87, and f-measure was 0.92. To our knowledge, these results are the first application of text mining to extract and associate dendrimer property terms and their corresponding numeric values. |
url |
http://europepmc.org/articles/PMC3879259?pdf=render |
work_keys_str_mv |
AT davidejones automaticextractionofnanoparticlepropertiesusingnaturallanguageprocessingnanosifteranapplicationtoacquirepamamdendrimerproperties AT seanigo automaticextractionofnanoparticlepropertiesusingnaturallanguageprocessingnanosifteranapplicationtoacquirepamamdendrimerproperties AT johnhurdle automaticextractionofnanoparticlepropertiesusingnaturallanguageprocessingnanosifteranapplicationtoacquirepamamdendrimerproperties AT juliocfacelli automaticextractionofnanoparticlepropertiesusingnaturallanguageprocessingnanosifteranapplicationtoacquirepamamdendrimerproperties |
_version_ |
1724982723669393408 |