Close
Metadata

@InProceedings{OliveiraPedrDias:2020:FuBLEn,
               author = "Oliveira, Gabriel Bianchin de and Pedrini, Helio and Dias, 
                         Zanoni",
          affiliation = "Institute of Computing, University of Campinas, Campinas, SP, 
                         Brazil, 13083-852 and Institute of Computing, University of 
                         Campinas, Campinas, SP, Brazil, 13083-852 and Institute of 
                         Computing, University of Campinas, Campinas, SP, Brazil, 
                         13083-852",
                title = "Fusion of BLAST and Ensemble of Classifiers for Protein Secondary 
                         Structure Prediction",
            booktitle = "Proceedings...",
                 year = "2020",
               editor = "Musse, Soraia Raupp and Cesar Junior, Roberto Marcondes and 
                         Pelechano, Nuria and Wang, Zhangyang (Atlas)",
         organization = "Conference on Graphics, Patterns and Images, 33. (SIBGRAPI)",
            publisher = "IEEE Computer Society",
              address = "Los Alamitos",
             keywords = "Protein Structure Prediction, Classifier Ensemble, Amino Acid 
                         Sequence.",
             abstract = "The 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.",
  conference-location = "Virtual",
      conference-year = "Nov. 7-10, 2020",
             language = "en",
           targetfile = "PID6614063.pdf",
        urlaccessdate = "2021, Jan. 19"
}


Close