Show/Hide Menu
Hide/Show Apps
anonymousUser
Logout
Türkçe
Türkçe
Search
Search
Login
Login
OpenMETU
OpenMETU
About
About
Open Science Policy
Open Science Policy
Communities & Collections
Communities & Collections
Help
Help
Frequently Asked Questions
Frequently Asked Questions
Videos
Videos
Thesis submission
Thesis submission
Publication submission with DOI
Publication submission with DOI
Publication submission
Publication submission
Contact us
Contact us
Farklı nitelikteki biyolojik ağların entegrasyonu ve yerel topolojik özellik vektörleri tabanlı karşılaştırılması
Download
TVRVM01qUTA.pdf
Date
2016
Author
Aydın Son, Yeşim
Akarsu, Andaç
Can, Tolga
Metadata
Show full item record
This work is licensed under a
Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License
.
Item Usage Stats
11
views
6
downloads
Cite This
In this project, we developed a framework for the analysis of integrated genome-scale networks using using directed graphlet signatures. In addition, we developed a novel graph layout algorithm specific for visualizing aligned networks. Analysis of integrated genome-scale networks is a challenging problem due to heterogeneity of high-throughput data. There are several topological measures, such as graphlet counts, for characterization of biological networks. In this project, we present methods for counting small sub-graph patterns in integrated genome-scale networks which are modeled as labeled multidigraphs. We have obtained physical, regulatory, and metabolic interactions between H. sapiens proteins from the Pathway Commons database. The integrated network is filtered for tissue/disease specific proteins by using a large-scale human transcriptional profiling study, resulting in several tissue and disease specific sub-networks. We have applied and extended the idea of graphlet counting in undirected protein-protein interaction (PPI) networks to directed multi-labeled networks and represented each network as a vector of graphlet counts. Graphlet counts are assessed for statistical significance by comparison against a set of randomized networks. We present our results on analysis of differential graphlets between different conditions and on the utility of graphlet count vectors for clustering multiple condition specific networks. Our results show that there are numerous statistically significant graphlets in integrated biological networks and the graphlet signature vector can be used as an effective representation of a multi-labeled network for clustering and systems level analysis of tissue/disease specific networks. In addition, the proposed graph layout algorithm can be used to visualize the similarities and differences between aligned regions of these networks
Subject Keywords
Integrated bilogical networks
,
Network comparison
,
Directed graphlets
,
Visualization of network alignments
URI
https://app.trdizin.gov.tr/publication/project/detail/TVRVM01qUTA
https://hdl.handle.net/11511/49916
Collections
Department of Computer Engineering, Project and Design
Citation Formats
IEEE
ACM
APA
CHICAGO
MLA
BibTeX
Y. Aydın Son, A. Akarsu, and T. Can, “Farklı nitelikteki biyolojik ağların entegrasyonu ve yerel topolojik özellik vektörleri tabanlı karşılaştırılması,” 2016. Accessed: 00, 2020. [Online]. Available: https://app.trdizin.gov.tr/publication/project/detail/TVRVM01qUTA.