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Int. J. Mol. Sci. 2016, 17(6), 862; doi:10.3390/ijms17060862

Review on Graph Clustering and Subgraph Similarity Based Analysis of Neurological Disorders

1,2,* , 3
and
1,2,*
1
Department of Computer Science, Stony Brook University, Stony Brook, NY 11794, USA
2
Department of Computer Science, State University New York Korea, Incheon 406-840, Korea
3
Korea Institute of Science and Technology Information, 245 Daehak-ro, Yuseong-gu, Daejeon 34141, Korea
*
Authors to whom correspondence should be addressed.
Academic Editor: Kurt A. Jellinger
Received: 18 March 2016 / Revised: 10 May 2016 / Accepted: 24 May 2016 / Published: 1 June 2016
(This article belongs to the Section Molecular Pathology, Diagnostics, and Therapeutics)
View Full-Text   |   Download PDF [2424 KB, uploaded 1 June 2016]   |  

Abstract

How can complex relationships among molecular or clinico-pathological entities of neurological disorders be represented and analyzed? Graphs seem to be the current answer to the question no matter the type of information: molecular data, brain images or neural signals. We review a wide spectrum of graph representation and graph analysis methods and their application in the study of both the genomic level and the phenotypic level of the neurological disorder. We find numerous research works that create, process and analyze graphs formed from one or a few data types to gain an understanding of specific aspects of the neurological disorders. Furthermore, with the increasing number of data of various types becoming available for neurological disorders, we find that integrative analysis approaches that combine several types of data are being recognized as a way to gain a global understanding of the diseases. Although there are still not many integrative analyses of graphs due to the complexity in analysis, multi-layer graph analysis is a promising framework that can incorporate various data types. We describe and discuss the benefits of the multi-layer graph framework for studies of neurological disease. View Full-Text
Keywords: graph clustering; graph similarity; neurological disease; biological network; structural brain network; functional brain network; multi-layer graphs graph clustering; graph similarity; neurological disease; biological network; structural brain network; functional brain network; multi-layer graphs
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Thomas, J.; Seo, D.; Sael, L. Review on Graph Clustering and Subgraph Similarity Based Analysis of Neurological Disorders. Int. J. Mol. Sci. 2016, 17, 862.

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