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
Inference of the JAK-STAT gene network via graphical models
Date
2011-06-01
Author
Purutçuoğlu Gazi, Vilda
Weber, Gerhard William
Metadata
Show full item record
Item Usage Stats
115
views
0
downloads
Cite This
URI
https://hdl.handle.net/11511/81826
https://www.slideshare.net/ssakpi/inference-of-the-jakstat-gene-network-via-graphical-models
Collections
Department of Statistics, Conference / Seminar
Suggestions
OpenMETU
Core
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 ...
Inference of Biological Networks via Random Forest Algorithm
Purutçuoğlu Gazi, Vilda (2015-06-01)
Inference of switching networks by using a piecewise linear formulation
Akçay, Didem; Öktem, Hakan; Department of Scientific Computing (2005)
Inference of regulatory networks has received attention of researchers from many fields. The challenge offered by this problem is its being a typical modeling problem under insufficient information about the process. Hence, we need to derive the apriori unavailable information from the empirical observations. Modeling by inference consists of selecting or defining the most appropriate model structure and inferring the parameters. An appropriate model structure should have the following properties. The model...
Inference of piecewise linear systems with an improved method employing jump detection
Selçuk, Ahmet Melih; Öktem, Hakan; Department of Scientific Computing (2007)
Inference of regulatory relations in dynamical systems is a promising active research area. Recently, most of the investigations in this field have been stimulated by the researches in functional genomics. In this thesis, the inferential modeling problem for switching hybrid systems is studied. The hybrid systems refers to dynamical systems in which discrete and continuous variables regulate each other, in other words the jumps and flows are interrelated. In this study, piecewise linear approximations are u...
Inference of large-scale networks via statistical approaches
Ayyıldız Demirci, Ezgi; Purutçuoğlu Gazi, Vilda; Department of Statistics (2019)
In system biology, the interactions between components such as genes, proteins, can be represented by a network. To understand the molecular mechanism of complex biological systems, construction of their networks plays a crucial role. However, estimation of these networks is a challenging problem because of their high dimensional and sparse structures. The Gaussian graphical model (GGM) is widely used approach to construct the undirected networks. GGM define the interactions between species by using the con...
Citation Formats
IEEE
ACM
APA
CHICAGO
MLA
BibTeX
V. Purutçuoğlu Gazi and G. W. Weber, “Inference of the JAK-STAT gene network via graphical models,” 2011, Accessed: 00, 2021. [Online]. Available: https://hdl.handle.net/11511/81826.