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Reference TypeConference Paper (Conference Proceedings)
Sitesibgrapi.sid.inpe.br
Identifier8JMKD3MGPEW34M/4395EF2
Repositorysid.inpe.br/sibgrapi/2020/09.15.23.42
Last Update2020:09.15.23.42.37 helio@ic.unicamp.br
Metadatasid.inpe.br/sibgrapi/2020/09.15.23.42.37
Metadata Last Update2020:10.28.20.46.49 administrator
Citation KeyOliveiraPedrDias:2020:FuBLEn
TitleFusion of BLAST and Ensemble of Classifiers for Protein Secondary Structure Prediction
FormatOn-line
Year2020
DateNov. 7-10, 2020
Access Date2021, Jan. 19
Number of Files1
Size218 KiB
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Author1 Oliveira, Gabriel Bianchin de
2 Pedrini, Helio
3 Dias, Zanoni
Affiliation1 Institute of Computing, University of Campinas, Campinas, SP, Brazil, 13083-852
2 Institute of Computing, University of Campinas, Campinas, SP, Brazil, 13083-852
3 Institute of Computing, University of Campinas, Campinas, SP, Brazil, 13083-852
EditorMusse, Soraia Raupp
Cesar Junior, Roberto Marcondes
Pelechano, Nuria
Wang, Zhangyang (Atlas)
e-Mail Addresshelio@ic.unicamp.br
Conference NameConference on Graphics, Patterns and Images, 33 (SIBGRAPI)
Conference LocationVirtual
Book TitleProceedings
PublisherIEEE Computer Society
Publisher CityLos Alamitos
Tertiary TypeFull Paper
History2020-09-15 23:42:37 :: helio@ic.unicamp.br -> administrator ::
2020-10-28 20:46:49 :: administrator -> helio@ic.unicamp.br :: 2020
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Is the master or a copy?is the master
Content Stagecompleted
Transferable1
Content TypeExternal Contribution
KeywordsProtein Structure Prediction, Classifier Ensemble, Amino Acid Sequence.
AbstractThe prediction of protein secondary structure has great relevance in the analysis of global protein folding. In this work, we present a method for protein secondary structure prediction using the fusion of BLAST and the ensemble of local and global classifiers. We used the amino acid sequence and sequence similarity information available in the datasets and we explored other amino acid characteristics. In order to evaluate our method, we used the files from PDB (only from the year 2018), as well as CB6133 and CB513 datasets. We achieved 87.7%, 82.4% and 85.6% Q8 accuracy on PDB 2018, CB6133 and CB513 proteins using the amino acid sequence and amino acid biological properties, 84.7% and 87.5% Q8 accuracy on CB6133 and CB513 proteins using the amino acid sequence and similarity sequence information and 92.5% Q3 accuracy on PDB 2018 proteins using the amino acid sequence and amino acid biological properties. Our method presented competitive results using only BLAST and only the ensemble of classifiers. The fusion of both approaches achieved superior results compared to state-of-the-art approaches.
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data URLhttp://urlib.net/rep/8JMKD3MGPEW34M/4395EF2
zipped data URLhttp://urlib.net/zip/8JMKD3MGPEW34M/4395EF2
Languageen
Target FilePID6614063.pdf
User Grouphelio@ic.unicamp.br
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Mirror Repositorysid.inpe.br/banon/2001/03.30.15.38.24
Next Higher Units8JMKD3MGPEW34M/43G4L9S
Host Collectionsid.inpe.br/banon/2001/03.30.15.38
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Empty Fieldsaccessionnumber archivingpolicy archivist area callnumber copyholder copyright creatorhistory descriptionlevel dissemination doi edition electronicmailaddress group holdercode isbn issn label lineage mark nextedition notes numberofvolumes orcid organization pages parameterlist parentrepositories previousedition previouslowerunit progress project readergroup readpermission resumeid rightsholder secondarydate secondarykey secondarymark secondarytype serieseditor session shorttitle sponsor subject tertiarymark type url versiontype volume
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