Show/Hide Menu
Hide/Show Apps
Logout
Türkçe
Türkçe
Search
Search
Login
Login
OpenMETU
OpenMETU
About
About
Open Science Policy
Open Science Policy
Open Access Guideline
Open Access Guideline
Postgraduate Thesis Guideline
Postgraduate Thesis Guideline
Communities & Collections
Communities & Collections
Help
Help
Frequently Asked Questions
Frequently Asked Questions
Guides
Guides
Thesis submission
Thesis submission
MS without thesis term project submission
MS without thesis term project submission
Publication submission with DOI
Publication submission with DOI
Publication submission
Publication submission
Supporting Information
Supporting Information
General Information
General Information
Copyright, Embargo and License
Copyright, Embargo and License
Contact us
Contact us
CANCER DRIVER SUBNETWORK IDENTIFICATION BY NETWORK DISMANTLING METHODS
Download
fatmaulkem_kasapoglu_tez.pdf
Date
2021-9
Author
Kasapoğlu, Fatma Ülkem
Metadata
Show full item record
This work is licensed under a
Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License
.
Item Usage Stats
299
views
420
downloads
Cite This
Tissue-specific protein protein interaction networks(TSPPI) are important for studying tissue-based cellular processes and protein functions. As the functions of tissues are various, the proteins they contain and the functions of these proteins also different than each other. Thus, these networks may help clinical studies both in tissue-specific cancer prediction by identification of the important groups in TSPPI. In this study, we aim to detect driver subnetworks in various TSPPIs and to understand effectiveness of the driver subnetworks. We performed iterative centrality attacks, Generalized Network Dismantling(GND), GND with Reinsertion(GNDR) on breast, liver, lymph node, ovary, and peripheral nerve TSPPIs from TissueNet v2 database. After the comparison by using driver gene information of each TSPPI from the Cancer Genome Interpreter and cBioPortal databases, we applied Personalized PageRank to each resulting TSPPI subnetworks to get more compact and robust subnetworks. Finally, we performed enrichment analysis for the proposed driver subnetworks. We found that genes in final subnetworks were enriched in both common and tissue-based cancer related pathways. As a result, we found that there was not optimal attack strategy for all TSPPIs. However, it is possible to obtain promising results for the cancer research by the comparison of these strategies.
Subject Keywords
Tissue-Specific Protein Protein Interaction Networks
,
Subnetwork Identification
,
Network Attacks
,
Network Dismantling
URI
https://hdl.handle.net/11511/92143
Collections
Graduate School of Informatics, Thesis
Suggestions
OpenMETU
Core
Prediction of protein subcellular localization based on primary sequence data
Özarar, Mert; Atalay, Mehmet Volkan; Department of Computer Engineering (2003)
Subcellular localization is crucial for determining the functions of proteins. A system called prediction of protein subcellular localization (P2SL) that predicts the subcellular localization of proteins in eukaryotic organisms based on the amino acid content of primary sequences using amino acid order is designed. The approach for prediction is to nd the most frequent motifs for each protein in a given class based on clustering via self organizing maps and then to use these most frequent motifs as features...
Distance-based Indexing of Residue Contacts for Protein Structure Retrieval and Alignment
Sacan, Ahmet; Toroslu, İsmail Hakkı; Ferhatosmanoglu, Hakan (2008-10-10)
New protein structures are continuously being determined with the hope of deriving insights into the function and mechanisms of proteins, and consequently, protein structure repositories are growing by leaps and bounds. However, we are still far from having the right methods for sensitive and effective use of the available structural data. The fact that current structural analysis tools are impractical for large-scale applications have given rise to several approaches that try to quickly identify candidate ...
Multi-view subcellular localization prediction of human proteins
Özsarı, Gökhan; Atalay, M. Volkan.; Department of Computer Engineering (2019)
Determining the subcellular localization of proteins is crucial for Understanding the functions of proteins, drug targeting, systems biology, and proteomics research. Experimental validation of subcellular localization is an expensive and challenging process. There exist several computational methods for automated prediction of protein subcellular localization; however, there is still room for better performance. Here, we propose a multi-view SVM-based approach that provides predictions for human proteins. ...
Structural characterization of recombinant bovine Go alpha by spectroscopy and homology modeling
MEGA TİBER, PINAR; Orun, Oya; Nacar, Cevdet; Sezerman, Ugur Osman; Severcan, Feride; Severcan, Mete; Matagne, Andre; KAN, BEKİ (2011-01-01)
Go, a member of heterotrimeric guanine nucleotide-binding proteins, is the most abundant form of G protein in the central and peripheral nervous systems. Go alpha has a significant role in neuronal development and function but its signal transduction mechanism remains to be clarified.
Enzyme prediction with word embedding approach
Akın, Erkan; Atalay, M. Volkan.; Department of Computer Engineering (2019)
Information such as molecular function, biological process, and cellular localization can be inferred from the protein sequence. However, protein sequences vary in length. Therefore, the sequence itself cannot be used directly as a feature vector for pattern recognition and machine learning algorithms since these algorithms require fixed length feature vectors. We describe an approach based on the use of the Word2vec model, more specifically continuous skip-gram model to generate the vector representation o...
Citation Formats
IEEE
ACM
APA
CHICAGO
MLA
BibTeX
F. Ü. Kasapoğlu, “CANCER DRIVER SUBNETWORK IDENTIFICATION BY NETWORK DISMANTLING METHODS,” M.S. - Master of Science, Middle East Technical University, 2021.