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(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

Discriminating Different Classes of Biological Networks by Analyzing the Graphs Spectra Distribution

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Takahashi, Daniel Yasumasa [1, 2] ; Sato, Joao Ricardo [3] ; Ferreira, Carlos Eduardo [4] ; Fujita, Andre [4]
Total Authors: 4
[1] Princeton Univ, Dept Psychol, Princeton, NJ 08544 - USA
[2] Princeton Univ, Inst Neurosci, Princeton, NJ 08544 - USA
[3] Fed Univ ABC, Ctr Math Computat & Cognit, Sao Paulo - Brazil
[4] Univ Sao Paulo, Dept Comp Sci, Inst Math & Stat, Santo Andre, SP - Brazil
Total Affiliations: 4
Document type: Journal article
Source: PLoS One; v. 7, n. 12 DEC 19 2012.
Web of Science Citations: 16

The brain's structural and functional systems, protein-protein interaction, and gene networks are examples of biological systems that share some features of complex networks, such as highly connected nodes, modularity, and small-world topology. Recent studies indicate that some pathologies present topological network alterations relative to norms seen in the general population. Therefore, methods to discriminate the processes that generate the different classes of networks (e. g., normal and disease) might be crucial for the diagnosis, prognosis, and treatment of the disease. It is known that several topological properties of a network (graph) can be described by the distribution of the spectrum of its adjacency matrix. Moreover, large networks generated by the same random process have the same spectrum distribution, allowing us to use it as a ``fingerprint{''}. Based on this relationship, we introduce and propose the entropy of a graph spectrum to measure the ``uncertainty{''} of a random graph and the Kullback-Leibler and Jensen-Shannon divergences between graph spectra to compare networks. We also introduce general methods for model selection and network model parameter estimation, as well as a statistical procedure to test the nullity of divergence between two classes of complex networks. Finally, we demonstrate the usefulness of the proposed methods by applying them to (1) protein-protein interaction networks of different species and (2) on networks derived from children diagnosed with Attention Deficit Hyperactivity Disorder (ADHD) and typically developing children. We conclude that scale-free networks best describe all the protein-protein interactions. Also, we show that our proposed measures succeeded in the identification of topological changes in the network while other commonly used measures (number of edges, clustering coefficient, average path length) failed. (AU)

FAPESP's process: 11/07762-8 - Granger causality for sets of time series: development of methodologies to model selection and extensions in the frequency domain with applications to molecular biology and neuroscience
Grantee:André Fujita
Support type: Regular Research Grants
FAPESP's process: 10/01394-4 - Statistical and computational methods for discriminating of anatomic changes, mental states and identification of brain connectivity: an integrative approach based on MRI, fMRI and EEG
Grantee:João Ricardo Sato
Support type: Regular Research Grants