Copula Gaussian graphical modeling of biological networks and Bayesian inference of model parameters

Date

2019-07-01

Author

Farnoudkia, H.
Purutçuoğlu Gazi, Vilda

Metadata

Show full item record

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Item Usage Stats

133
views

0
downloads

A proper understanding of complex biological networks facilitates a better perception of those diseases that plague systems and efficient production of drug targets, which is one of the major research questions under the personalized medicine. However, the description of these complexities is challenging due to the associated continuous, high-dimensional, correlated and very sparse data. The Copula Gaussian Graphical Model (CGGM), which is based on the representation of the multivariate normal distribution via marginal and copula terms, is one of the successful modeling approaches to presenting such types of problematic datasets. This study shows its novelty by using CGGM in modeling the steady-state activation of biological networks and making inference of the model parameters under the Bayesian setting. In this regard, the Reversible Jump Markov Chain Monte Carlo (RJMCMC) algorithm is suggested in order to estimate the plausible interactions (conditional dependence) between the systems' elements, which are proteins or genes. Furthermore, the open-source R codes of RJMCMC are generated for CGGM in different dimensional networks. In this regard, real datasets are applied, and the accuracy of estimates via F-measure is evaluated. From the results, it is observed that CGGM with RJMCMC is successful in presenting real and complex systems with higher accuracy. (C) 2019 Sharif University of Technology. All rights reserved.

Subject Keywords

Copula Gaussian graphical model, Systems biology, F-measure, Biological networks, Reversible jump Markov chain Monte Carlo algorithm

URI

https://hdl.handle.net/11511/56184

Journal

SCIENTIA IRANICA

DOI

https://doi.org/10.24200/sci.2019.5071.1076

Collections

Department of Statistics, Article

Suggestions

OpenMETU
Core

Graphical models in inference of biological networks Farnoudkia, Hajar; Purutçuoğlu Gazi, Vilda; Department of Statistics (2020) In recent years, particularly, on the studies about the complex system’s diseases, better understanding the biological systems and observing how the system’s behaviors, which are affected by the treatment or similar conditions, accelerate with the help of the explanation of these systems via the mathematical modeling. Gaussian Graphical Models (GGM) is a model that describes the relationship between the system’s elements via the regression and represents the states of the system via the multivariate Gaussia...
Model comparison for gynecological cancer datasets and selection of threshold value Bahçivancı, Başak; Purutçuoğlu Gazi, Vilda; Department of Statistics (2019) Cancer is a very common system’s disease with its structural and functional complexities caused by high dimension and serious correlation of genes as well as sparsity of gene interactions. Hereby, different mathematical models have been suggested in the literature to unravel these challenges. Among many alternates, in this study we use the Gaussian graphical model, Gaussian copula graphical model and loop-based multivariate adaptive regression splines with/without interaction models due to their advantages ...
RANKPCSF: A DISEASE MODULE IDENTIFICATION METHOD BY INTEGRATING NETWORK PROPAGATION WITH PRIZE-COLLECTING STEINER FOREST Eskin, Arda; Otlu Sarıtaş, Burçak; Tunçbağ, Nurcan; Department of Bioinformatics (2022-9-2) Identification of disease modules may lead to a better understanding on the progression of diseases, finding more accurate biomarkers, and drug targets. In this study, we develop a hybrid method – RANKPCSF- combining the strength of Steiner tree and diffusion approaches and release a new network reconstruction approach. RANKPCSF is capable to integrate multi-omic data (including phosphoproteomic and transcriptomic) with a reference interactome to unveil the optimal disease-associated network. We have compar...
COMPUTATIONAL MECHANICS FOR SOFT BIOLOGICAL TISSUES Altun, Cem; Dal, Hüsnü; Department of Mechanical Engineering (2023-1-17) Computational biomechanics is an active research area, not only to understand the mechanisms behind the behaviours of biological tissues but also to develop medical techniques for surgeries, rehabilitations, and diseases. The thesis mainly composed of two parts namely, growth-induced instabilities and dispersion-type anisotropic viscoelasticity for soft biological tissues. In the first part of the thesis, planar growth-induced instabilities for a three-dimensional bilayer-type confined tissue is examined. F...
CROssBAR: comprehensive resource of biomedical relations with knowledge graph representations DOĞAN, TUNCA; Atas, Heval; Joshi, Vishal; Atakan, Ahmet; Rifaioglu, Ahmet Sureyya; Sinoplu, Esra; Nightingale, Andrew; Saidi, Rabie; Volynkin, Vladimir; Zellner, Hermann; Cetin-Atalay, Rengul; Martin, Maria; Atalay, Volkan (2021-09-01) Systemic analysis of available large-scale biological/biomedical data is critical for studying biological mechanisms, and developing novel and effective treatment approaches against diseases. However, different layers of the available data are produced using different technologies and scattered across individual computational resources without any explicit connections to each other, which hinders extensive and integrative multi-omics-based analysis. We aimed to address this issue by developing a new data in...

Citation Formats

H. Farnoudkia and V. Purutçuoğlu Gazi, “Copula Gaussian graphical modeling of biological networks and Bayesian inference of model parameters,” SCIENTIA IRANICA, pp. 2495–2505, 2019, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/56184.