Review on Graph Clustering and Subgraph Similarity Based Analysis of Neurological Disorders.
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.
Department of Computer Science, Stony Brook University, Stony Brook, NY 11794, USA. jaya.thomas@sunykorea.ac.kr.|Department of Computer Science, State University New York Korea, Incheon 406-840, Korea. jaya.thomas@sunykorea.ac.kr.
Jaya
J
Thomas
Korea Institute of Science and Technology Information, 245 Daehak-ro, Yuseong-gu, Daejeon 34141, Korea. dmseo@kisti.re.kr.
Dongmin
D
Seo
Department of Computer Science, Stony Brook University, Stony Brook, NY 11794, USA. sael@cs.stonybrook.edu.|Department of Computer Science, State University New York Korea, Incheon 406-840, Korea. sael@cs.stonybrook.edu.
Lee
L
Sael
D000818:Animals; D001921:Brain / Q000378:metabolism / Q000503:physiopathology; D016000:Cluster Analysis; D003198:Computer Simulation; D053263:Gene Regulatory Networks; D006801:Humans; D053858:Metabolic Networks and Pathways; D008954:Models, Biological; D009415:Nerve Net; D009422:Nervous System Diseases / Q000209:etiology* / Q000378:metabolism* / Q000473:pathology; D009434:Neural Pathways; D060066:Protein Interaction Maps; D015398:Signal Transduction
biological network; functional brain network; graph clustering; graph similarity; multi-layer graphs; neurological disease; structural brain network