Gene network coherence based on prior knowledge using direct and indirect relationships
Affiliation:
1. Spectroscopy Department, Physics Division, National Research Centre, 33 Elbehouth St., Dokki, 12311, Cairo, Egypt;2. Glass Research Department, National Research Centre, 33 Elbehouth St., Dokki, 12311, Cairo, Egypt;3. National Center for Radiation Technology, Nasr City, Cairo, Egypt;1. Institute of Physical Chemistry and Chemical Physics, Faculty of Chemical and Food Technology, Slovak University of Technology in Bratislava, Radlinského 9, SK-812 37 Bratislava, Slovak Republic;2. Department of Chemistry, Faculty of Natural Sciences, Constantine the Philosopher University in Nitra, Trieda A. Hlinku 1, SK-949 74 Nitra, Slovak Republic;3. LAQV@REQUIMTE, Department of Chemistry and Biochemistry, Faculty of Sciences, University of Porto, Rua do Campo Alegre 687, 4169-007 Porto, Portugal;4. Institute of Organic Chemistry, Catalysis and Petrochemistry, Faculty of Chemical and Food Technology, Slovak University of Technology in Bratislava, Radlinského 9, SK-812 37 Bratislava, Slovak Republic;1. Department of Science, Faculty of Education, Dicle University, 21280, Diyarbakır, Turkey;2. Department of Oncology, Faculty of Medicine, Dicle University, 21280, Diyarbakır, Turkey;1. State Key Laboratory of Nuclear Physics and Technology, Institute of Heavy Ion Physics, School of Physics, Peking University, Beijing, 100871, China;2. Beijing National Laboratory for Molecular Sciences, State Key Laboratory for Rare Earth Materials Chemistry and Applications, College of Chemistry and Molecular Engineering, Peking University, Beijing, 100871, China;3. Department of Physics, Capital Normal University, Beijing Advanced Innovation Center of Imaging Technology, Key Lab of Terahertz Optoelectronics, Ministry of Education, Beijing, 100048, China
Abstract:
Gene networks (GNs) have become one of the most important approaches for modeling biological processes. They are very useful to understand the different complex biological processes that may occur in living organisms. Currently, one of the biggest challenge in any study related with GN is to assure the quality of these GNs. In this sense, recent works use artificial data sets or a direct comparison with prior biological knowledge. However, these approaches are not entirely accurate as they only take into account direct gene–gene interactions for validation, leaving aside the weak (indirect) relationships.We propose a new measure, named gene network coherence (GNC), to rate the coherence of an input network according to different biological databases. In this sense, the measure considers not only the direct gene–gene relationships but also the indirect ones to perform a complete and fairer evaluation of the input network. Hence, our approach is able to use the whole information stored in the networks. A GNC JAVA-based implementation is available at: http://fgomezvela.github.io/GNC/.The results achieved in this work show that GNC outperforms the classical approaches for assessing GNs by means of three different experiments using different biological databases and input networks. According to the results, we can conclude that the proposed measure, which considers the inherent information stored in the direct and indirect gene–gene relationships, offers a new robust solution to the problem of GNs biological validation.
