Dataset Information

A statistical physics approach for disease module detection.

ABSTRACT: Extensive evidence indicates that the pathobiological processes of a complex disease are associated with perturbation in specific neighborhoods of the human protein-protein interaction (PPI) network (also known as the interactome), often referred to as the disease module. Many computational methods have been developed to integrate the interactome and omics profiles to extract context-dependent disease modules. Yet, existing methods all have fundamental limitations in terms of rigor and/or efficiency. Here, we developed a statistical physics approach based on the random-field Ising model (RFIM) for disease module detection, which is both mathematically rigorous and computationally efficient. We applied our RFIM approach to genome-wide association studies (GWAS) of ten complex diseases to examine its performance for disease module detection. We found that our RFIM approach outperforms existing methods in terms of computational efficiency, connectivity of disease modules, and robustness to the interactome incompleteness.

SUBMITTER: Wang XW

PROVIDER: S-EPMC9712625 | biostudies-literature | 2022 Oct

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

A statistical physics approach for disease module detection.

Wang Xu-Wen XW Qiao Dandi D Cho Michael H MH DeMeo Dawn L DL Silverman Edwin K EK Liu Yang-Yu YY

Genome research 20221011 10

Extensive evidence indicates that the pathobiological processes of a complex disease are associated with perturbation in specific neighborhoods of the human protein-protein interaction (PPI) network (also known as the interactome), often referred to as the disease module. Many computational methods have been developed to integrate the interactome and omics profiles to extract context-dependent disease modules. Yet, existing methods all have fundamental limitations in terms of rigor and/or effici ...[more]

PMID: 36220609

Similar Datasets

Project description:The production of cobalt oxide nanoparticles and their use in the adsorption of methylene blue (MB) from solution is described in the paper. The X-ray diffraction patterns show that the synthesized cobalt oxide nanoparticles have a crystalline cubic structure. The study of the adsorption of methylene blue onto the cobalt oxide nanoparticles involved determining the contact time and initial concentration of the adsorption of MB on the adsorbent. The kinetics of adsorption were analyzed using two kinetic models (pseudo-first order and pseudo-second order), and the pseudo-second-order model was found to be the most appropriate for describing the behavior of the adsorption. This study indicates that the MLTS (monolayer with the same number of molecules per site) model is the most suitable model for describing methylene blue/cobalt oxide systems, and the parameter values help to further understand the adsorption process with the steric parameters. Indicating that methylene blue is horizontally adsorbed onto the surface of the cobalt oxide, which is bonded to two different receptor sites. Regarding the temperature effect, it was found that the adsorption capacity increased, with the experimental value ranging from 313.7 to 405.3 mg g-1, while the MLTS predicted 313.32 and 408.16 mg g-1. From the thermodynamic functions, high entropy was found around 280 mg L-1 concentration. For all concentrations and temperatures examined, the Gibbs free energy and enthalpy of adsorption were found to be negative and positive, respectively, suggesting that the system is spontaneous and endothermic. According to this study's findings, methylene blue adsorption onto cobalt oxide nanoparticles happens via the creation of a monolayer, in which the same amount of molecules are adsorbed at two distinct locations. The findings shed light on the methylene blue adsorption process onto cobalt oxide nanoparticles, which have a variety of uses, including the remediation of wastewater.

Dataset Information

A statistical physics approach for disease module detection.

Publications

A statistical physics approach for disease module detection.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets