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A parallel classification framework for protein fold recognition

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Abstract

Proteins’ tertiary structure, which is determined by its amino acid sequence via the protein folding process, have essential role in the function of protein. Protein fold recognition is one of the interesting studies in bioinformatics. In this paper, to address this issue, we propose a Feature Selection (FS) method based on Map_Reduce framework and Vortex Search Algorithm (VSA). FS is one of the most important steps of pre-processing data, which aims to select a variable subset of relevant features. In unparalleled mode and typical data, over hundreds of feature selection and dimension reduction algorithms have been provided such as Principle Component Analysis, Linear Discriminant Analysis, and so on. Nevertheless, these algorithms are not implemented for real-world applications when data instances increasing in three-dimensional: volume, velocity and variety that called Big Data, actually if we want to use previous feature selection methods on Big Data, volume of large and complex computing will be required. VSA was inspired from the vortex pattern created by the vortical flow of the stirred fluids. In Map_Reduce framework, Map and Reduce functions executed in parallel mode. In the proposed method, in each step of Map function, a VSA is employed to find an optimized subset of features and decrease feature search space. In the light of the above consideration, we evaluate the proposed method in classification of a benchmark dataset for protein fold recognition. The experimental results indicate that the proposed method improves prediction accuracy considerably.

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Notes

  1. https://archive.ics.uci.edu/ml/datasets/Audit+Data

  2. https://archive.ics.uci.edu/ml/datasets/Parkinson%27s+Disease+Classification

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Author information

Authors and Affiliations

  1. Department of Computer Engineering, School of Engineering, Azad University, Science and Research Branch, Tehran, Iran

    Elham Hekmatnia

  2. Department of Computer Science, School of Mathematics, Statistics and Computer Science, College of Science, University of Tehran, Tehran, Iran

    Hedieh Sajedi

  3. Department of Computer Engineering, School of Engineering, Azad University, South Tehran Branch, Tehran, Iran

    Ali Habib Agahi

Authors
  1. Elham Hekmatnia
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  2. Hedieh Sajedi
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  3. Ali Habib Agahi
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Correspondence to Hedieh Sajedi.

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Hekmatnia, E., Sajedi, H. & Habib Agahi, A. A parallel classification framework for protein fold recognition. Evol. Intel. 13, 525–535 (2020). https://doi.org/10.1007/s12065-020-00350-7

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