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The protein-protein interaction network alignment using recurrent neural network.

The main challenge of biological network alignment is that the problem of finding the alignments in two graphs is NP-hard. The discovery of protein-protein interaction (PPI) networks is of great importance in bioinformatics due to their utilization in identifying the cellular pathways, finding new m...

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Published in:Medical & Biological Engineering & Computing Vol. 59; no. 11/12; pp. 2263 - 2287
Main Authors: Mahdipour, Elham, Ghasemzadeh, Mohammad
Format: Journal Article
Published: Springer Nature Nov2021
Online Access:View this record in EBSCOhost
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      jtl: Medical & Biological Engineering & Computing
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      dt: Nov2021
      vid: 59
      iid: 11/12
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      pub: Springer Nature
      place: New York, New York
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        10.1007/s11517-021-02428-5
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        atl: The protein-protein interaction network alignment using recurrent neural network.
      aug:
        au:
          Mahdipour, Elham
          Ghasemzadeh, Mohammad
        affil: Computer Engineering Department at Khavaran Institute of Higher Education, Mashhad, Iran
      sug:
        subj:
          Molecular Probe Techniques
          Metabolic Networks and Pathways
          Bioinformatics
          Algorithms
      ab: The main challenge of biological network alignment is that the problem of finding the alignments in two graphs is NP-hard. The discovery of protein-protein interaction (PPI) networks is of great importance in bioinformatics due to their utilization in identifying the cellular pathways, finding new medicines, and disease recognition. In this regard, we describe the network alignment method in the form of a classification problem for the very first time and introduce a deep network that finds the alignment of nodes present in the two networks. We call this method RENA, which means Network Alignment using REcurrent neural network. The proposed solution consists of three steps; in the first phase, we obtain the sequence and topological similarities from the networks' structure. For the second phase, the dataset needed for the transformation of the problem into a classification problem is created from obtained features. In the third phase, we predict the nodes' alignment between two networks using deep learning. We used Biogrid dataset for RENA evaluation. The RENA method is compared with three classification approaches of support vector machine, K-nearest neighbors, and linear discriminant analysis. The experimental results demonstrate the efficiency of the RENA method and 100% accuracy in PPI network alignment prediction.
      pubtype: Academic Journal
      doctype: Journal Article
      ougenre: Article
    language: English
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