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
Scalable approach for effective control of gene regulatory networks
Date
2010-01-01
Author
Tan, Mehmet
Alhajj, Reda
Polat, Faruk
Metadata
Show full item record
This work is licensed under a
Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License
.
Item Usage Stats
207
views
0
downloads
Cite This
Objective: Interactions between genes are realized as gene regulatory networks (GRNs). The control of such networks is essential for investigating issues like different diseases. Control is the process of studying the states and behavior of a given system under different conditions. The system considered in this study is a gene regulatory network (GRN), and one of the most important aspects in the control of GRNs is scalability. Consequently, the objective of this study is to develop a scalable technique that facilitates the control of GRNs.
Subject Keywords
Medicine (miscellaneous)
,
Artificial Intelligence
URI
https://hdl.handle.net/11511/38800
Journal
ARTIFICIAL INTELLIGENCE IN MEDICINE
DOI
https://doi.org/10.1016/j.artmed.2009.10.002
Collections
Department of Computer Engineering, Article
Suggestions
OpenMETU
Core
Effective gene expression data generation framework based on multi-model approach
Sirin, Utku; Erdogdu, Utku; Polat, Faruk; TAN, MEHMET; Alhajj, Reda (Elsevier BV, 2016-06-01)
Objective: Overcome the lack of enough samples in gene expression data sets having thousands of genes but a small number of samples challenging the computational methods using them.
Integer linear programming based solutions for construction of biological networks
Eren Özsoy, Öykü; Can, Tolga; Department of Health Informatics (2014)
Inference of gene regulatory or signaling networks from perturbation experiments and gene expression assays is one of the challenging problems in bioinformatics. Recently, the inference problem has been formulated as a reference network editing problem and it has been show that finding the minimum number of edit operations on a reference network in order to comply with perturbation experiments is an NP-complete problem. In this dissertation, we propose linear programming based solutions for reconstruction o...
AN EFFICIENT DATABASE TRANSITIVE CLOSURE ALGORITHM
Toroslu, İsmail Hakkı; HENSCHEN, L (Springer Science and Business Media LLC, 1994-05-01)
The integration of logic rules and relational databases has recently emerged as an important technique for developing knowledge management systems. An important class of logic rules utilized by these systems is the so-called transitive closure rules, the processing of which requires the computation of the transitive closure of database relations referenced by these rules. This article presents a new algorithm suitable for computing the transitive closure of very large database relations. This algorithm proc...
Inference of Gene Regulatory Networks Via Multiple Data Sources and a Recommendation Method
Ozsoy, Makbule Gulcin; Polat, Faruk; Alhajj, Reda (2015-11-12)
Gene regulatory networks (GRNs) are composed of biological components, including genes, proteins and metabolites, and their interactions. In general, computational methods are used to infer the connections among these components. However, computational methods should take into account the general features of the GRNs, which are sparseness, scale-free topology, modularity and structure of the inferred networks. In this work, observing the common aspects between recommendation systems and GRNs, we decided to ...
INTEGRATION OF MACHINE LEARNING AND ENTROPY METHODS FOR POST-GENOME-WIDE ASSOCIATION STUDIES ANALYSIS
Yaldız, Burcu; Aydın Son, Yeşim; Department of Medical Informatics (2022-8-31)
Non-linear relationships between genotypes play an essential role in understanding the genetic interactions of complex disease traits. Genome-Wide Association Studies (GWAS) have revealed a statistical association between the SNPs in many complex diseases. As GWAS results could not thoroughly explain the genetic background of these disorders, Genome-Wide Interaction Studies started to gain importance. In recent years, various statistical approaches such as entropy-based methods have been suggested for revea...
Citation Formats
IEEE
ACM
APA
CHICAGO
MLA
BibTeX
M. Tan, R. Alhajj, and F. Polat, “Scalable approach for effective control of gene regulatory networks,”
ARTIFICIAL INTELLIGENCE IN MEDICINE
, pp. 51–59, 2010, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/38800.