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Journal of Integrative Bioinformatics

Editor-in-Chief: Schreiber, Falk / Hofestädt, Ralf

Managing Editor: Sommer, Björn

Ed. by Baumbach, Jan / Chen, Ming / Orlov, Yuriy / Allmer, Jens

Editorial Board: Giorgetti, Alejandro / Harrison, Andrew / Kochetov, Aleksey / Krüger, Jens / Ma, Qi / Matsuno, Hiroshi / Mitra, Chanchal K. / Pauling, Josch K. / Rawlings, Chris / Fdez-Riverola, Florentino / Romano, Paolo / Röttger, Richard / Shoshi, Alban / Soares, Siomar de Castro / Taubert, Jan / Tauch, Andreas / Yousef, Malik / Weise, Stephan / Hassani-Pak, Keywan


CiteScore 2017: 0.77

SCImago Journal Rank (SJR) 2017: 0.336

Open Access
Online
ISSN
1613-4516
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Volume 13, Issue 2

Issues

Feature Learning applied to the Estimation of Tensile Strength at Break in Polymeric Material Design

Cravero Fiorella
  • Planta Piloto de Ingeniería Química, Universidad Nacional del Sur – CONICET. La Carrindanga 7000, Bahía Blanca, Argentina
  • Other articles by this author:
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/ Martínez M. Jimena
  • Instituto de Ciencias e Ingeniería de la Computación, Universidad Nacional del Sur – CONICET. San Andrés 800 – Campus Palihue, 8000, Bahía Blanca, Argentina
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/ Vazquez Gustavo
  • Facultad de Ingeniería y Tecnologías, Universidad Católica del Uruguay. Av. 8 de Octubre 2738, Montevideo, Uruguay
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/ Mónica F. Díaz
  • Planta Piloto de Ingeniería Química, Universidad Nacional del Sur – CONICET. La Carrindanga 7000, Bahía Blanca, Argentina
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/ Ponzoni Ignacio
  • Corresponding author
  • Instituto de Ciencias e Ingeniería de la Computación, Universidad Nacional del Sur - CONICET. San Andrés 800 - Campus Palihue, 8000, Bahía Blanca, Argentina
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Published Online: 2017-04-20 | DOI: https://doi.org/10.1515/jib-2016-286

Summary

Several feature extraction approaches for QSPR modelling in Cheminformatics are discussed in this paper. In particular, this work is focused on the use of these strategies for predicting mechanical properties, which are relevant for the design of polymeric materials. The methodology analysed in this study employs a feature learning method that uses a quantification process of 2D structural characterization of materials with the autoencoder method. Alternative QSPR models inferred for tensile strength at break (a well-known mechanical property of polymers) are presented. These alternative models are contrasted to QSPR models obtained by feature selection technique by using accuracy measures and a visual analytic tool. The results show evidence about the benefits of combining feature learning approaches with feature selection methods for the design of QSPR models.

About the article

Published Online: 2017-04-20

Published in Print: 2016-06-01


Citation Information: Journal of Integrative Bioinformatics, Volume 13, Issue 2, Pages 15–29, ISSN (Online) 1613-4516, DOI: https://doi.org/10.1515/jib-2016-286.

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© 2016 The Author(s). Published by Journal of Integrative Bioinformatics.. This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. BY-NC-ND 4.0

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