Volume 10, Issue 2 (2019)
JMBS 2019, 10(2): 329-334 |
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Zare Mirak-Abad F, Ghorbanali Z. PRAF Framework for Global Protein-Protein Interaction Network Alignment. JMBS 2019; 10 (2) :329-334
URL: http://biot.modares.ac.ir/article-22-13493-en.html
URL: http://biot.modares.ac.ir/article-22-13493-en.html
1- Computer Science Department, Mathematics & Computer Science Faculty, Amirkabir University, Tehran, Iran, Computer Science Department, Mathematics & Computer Science Faculty, Amirkabir University, Tehran, Iran , f.zare@aut.ac.ir
2- Computer Science Department, Mathematics & Computer Science Faculty, Amirkabir University, Tehran, Iran
2- Computer Science Department, Mathematics & Computer Science Faculty, Amirkabir University, Tehran, Iran
Abstract: (4665 Views)
A biological network represents the interaction between a set of macromolecules to drive a particular biological process. In a biological environment, abnormalities happen not only in one molecule but also through a biological network. One of the most effective methods to detect anomaly is the comparison between healthy and diseased networks. In this regard, biological network alignment is one of the most efficient ways to find the difference between healthy and diseased cells. This problem, protein-protein interaction network alignment, has been raised in two main types: Local network alignment and Global network alignment. According to the NP-completeness of this problem, different non-deterministic approaches have been proposed to tackle the Global network alignment problem. Recently, NetAl has been introduced as a common algorithm to align two networks. Although this algorithm can align two networks at the appropriate time, it does not consider biological features. In this study, we present a new framework called PRAF to improve the results of network alignment algorithms such as NetAl by considering some biological features like gene ontology (GO).
Keywords: Bioinformatics, Protein-protein interaction networks, Alignment, Gene ontology, Clustering
Article Type: Research Paper |
Subject:
Agricultural Biotechnology
Received: 2018/02/4 | Accepted: 2018/03/3 | Published: 2018/06/20
Received: 2018/02/4 | Accepted: 2018/03/3 | Published: 2018/06/20
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