poster

Protein secondary structure prediction based on fusion of machine learning classifiers

Authors:

Gabriel Bianchin de Oliveira,

Zanoni DiasAuthors Info & Claims

SAC '21: Proceedings of the 36th Annual ACM Symposium on Applied Computing

Pages 26 - 29

https://doi.org/10.1145/3412841.3442067

Published: 22 April 2021 Publication History

Abstract

Protein secondary structure prediction plays an important role in protein folding and function classification. Although the works available in the literature present good results, protein secondary structure prediction is still an open problem. In this work, we present and discuss a fusion strategy using four different classifiers. The fusion is composed of bidirectional recurrent networks, random forests, Inception-v4 blocks and Inception recurrent networks. In order to evaluate our model, we used CB6133 dataset as training and testing. The fusion achieved 76.4% of Q8 accuracy using the amino acid sequence and similarity information on CB6133, surpassing state-of-the-art approaches.

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Cited By

de Oliveira GPedrini HDias Z(2021)Ensemble of Template-Free and Template-Based Classifiers for Protein Secondary Structure PredictionInternational Journal of Molecular Sciences10.3390/ijms22211144922:21(11449)Online publication date: 23-Oct-2021
https://doi.org/10.3390/ijms222111449

Index Terms

Protein secondary structure prediction based on fusion of machine learning classifiers
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Supervised learning
        Supervised learning by classification
    2. Machine learning approaches

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Published In

cover image ACM Conferences

SAC '21: Proceedings of the 36th Annual ACM Symposium on Applied Computing

March 2021

2075 pages

ISBN:9781450381048

DOI:10.1145/3412841

Conference Chairs:
Chih-Cheng Hung
Kennesaw State University
,
Jiman Hong
Soongsil University, South Korea
,
Program Chairs:
Alessio Bechini
University of Pisa, Italy
,
Eunjee Song
Baylor University

Copyright © 2021 Owner/Author.

Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Published: 22 April 2021

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SAC '21: The 36th ACM/SIGAPP Symposium on Applied Computing

March 22 - 26, 2021

Virtual Event, Republic of Korea

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Overall Acceptance Rate 1,650 of 6,669 submissions, 25%

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Cited By

de Oliveira GPedrini HDias Z(2021)Ensemble of Template-Free and Template-Based Classifiers for Protein Secondary Structure PredictionInternational Journal of Molecular Sciences10.3390/ijms22211144922:21(11449)Online publication date: 23-Oct-2021
https://doi.org/10.3390/ijms222111449

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