download0 view142
twitter facebook

공공누리This item is licensed Korea Open Government License

Title
Review on Graph Clustering and Subgraph Similarity Based Analysis of Neurological Disorders
Author(s)
Jaya ThomasLee Sael서동민
Publication Year
2016-06-01
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.
Keyword
graph clustering; graph similarity; neurological disease; biological netowrk; structural brain network; functional netowrk; multi-layer graphs
Journal Title
International Journal of Molecular Sciences
Citation Volume
17
ISSN
1422-0067
Files in This Item:
There are no files associated with this item.
Appears in Collections:
7. KISTI 연구성과 > 학술지 발표논문
URI
https://repository.kisti.re.kr/handle/10580/14498
http://www.ndsl.kr/ndsl/search/detail/article/articleSearchResultDetail.do?cn=NART78495093
Export
RIS (EndNote)
XLS (Excel)
XML

Browse