Title: Link prediction potentials for biological networks

Authors: Sadegh Sulaimany; Mohammad Khansari; Ali Masoudi-Nejad

Addresses: Department of Computer Engineering, University of Kurdistan, Sanandaj, Iran; Laboratory of Systems Biology and Bioinformatics (LBB), Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran ' Faculty of New Sciences and Technologies (FNST), University of Tehran, Tehran, Iran ' Laboratory of Systems Biology and Bioinformatics (LBB), Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran

Abstract: Improvement of biological networks reconstructed from high-throughput expression data is an important challenge in systems biology. Link prediction is a problem of interest in many application domains that can be used for this purpose. In this paper after a short review of several biological networks, we present the latest definition of the link prediction problem and review it from several viewpoints. With a comprehensive search in the literature using PubMed, Science Direct and Google Scholar databases, and carefully reviewing the related papers having the 'link prediction' plus at least one of the biological network terms in their title, abstract or keywords, we classify the results based on the graph type and major link prediction outlooks. Finally, we analyse the preformed researches to find new insights about potential uses in addition to understanding the current state, and propose several hints and directions for future works.

Keywords: link prediction; biological networks; biological link prediction; biological link mining.

DOI: 10.1504/IJDMB.2018.093684

International Journal of Data Mining and Bioinformatics, 2018 Vol.20 No.2, pp.161 - 184

Accepted: 03 May 2018
Published online: 27 Jul 2018 *

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