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AntiVPP 1.0: A portable tool for prediction of antiviral peptides.
Beltrán Lissabet, Jorge Félix; Belén, Lisandra Herrera; Farias, Jorge G.
Affiliation
  • Beltrán Lissabet JF; Universidad de La Frontera, Department of Chemical Engineering, Faculty of Engineering and Science, Temuco, Chile.
  • Belén LH; Universidad de La Frontera, Department of Chemical Engineering, Faculty of Engineering and Science, Temuco, Chile.
  • Farias JG; Universidad de La Frontera, Department of Chemical Engineering, Faculty of Engineering and Science, Temuco, Chile. Electronic address: jorge.farias@ufrontera.cl.
Comput Biol Med ; 107: 127-130, 2019 04.
Article in En | MEDLINE | ID: mdl-30802694
Viruses are worldwide pathogens with a high impact on the human population. Despite the constant efforts to fight viral infections, there is a need to discover and design new drug candidates. Antiviral peptides are molecules with confirmed activity and constitute excellent alternatives for the treatment of viral infections. In the present study, we developed AntiVPP 1.0, an accurate bioinformatic tool that uses the Random Forest algorithm for antiviral peptide predictions. The model of AntiVPP 1.0 for antiviral peptide predictions uses several features of 1088 peptides for training and validation. During the validation of the model we achieved the TPR = 0.87, SPC = 0.97, ACC = 0.93 and MCC = 0.87 performance measures, which were indicative of a robust model. AntiVPP 1.0 is a fast, accurate and intuitive software focused on the assessment of antiviral peptides candidates. AntiVPP 1.0 is available at https://github.com/bio-coding/AntiVPP.
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Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Antiviral Agents / Peptides / Software / Machine Learning Type of study: Prognostic_studies / Risk_factors_studies Language: En Journal: Comput Biol Med Year: 2019 Document type: Article Affiliation country: Chile Country of publication: United States

Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Antiviral Agents / Peptides / Software / Machine Learning Type of study: Prognostic_studies / Risk_factors_studies Language: En Journal: Comput Biol Med Year: 2019 Document type: Article Affiliation country: Chile Country of publication: United States