Differential network analysis between sex of the genes related to comorbidities of type 2 mellitus diabetes

Abstract

Background Some phenotypical changes may be related to changes in the associations among genes. The set of such associations is referred to as gene interaction (or association) networks. An association network represents the set of associations among genes in a given condition. Given two experimental conditions, Differential network analysis (DNA) algorithms analyse these differences by deriving a novel network representing the differences. Such algorithms receive as input experimental gene-expression data of two different conditions (e.g. healthy vs. diseased), then they derive experimental networks of associations among genes and, finally, they analyse differences among networks using statistical approaches. We explore the possibility to study possible rewiring due to sex factors, differently from classical approaches. Methods We apply DNA methods to evidence possible sex based differences on genes responsible for comorbidities of type 2 diabetes mellitus. Results Our analysis evidences the presence of differential networks in tissues that may explain the difference in the insurgence of comorbidities between males and females. Conclusion Main contributions of this work are (1) the definition of a novel framework of analysis able to shed light on the differences between males and females; (2) the identification of differential networks related to diabetes comorbidities.

Non Parametric Differential Network Analysis: A Tool for Unveiling Specific Molecular Signatures

“…The emergence of high-throughput technologies in genomics, proteomics, and non-coding RNA studies has revolutionized our understanding of how variations in the April 29, 2024 1/22 abundance of these biological molecules correlate with diseases [1]. This deluge of data has spurred the creation of innovative analytical techniques that adopt a system-level approach through the lens of network science.…”

“…Traditional methods often assume that gene expression data adhere to specific parametric distributions, such as Gaussian or Poisson distributions [1,[14][15][16]. However, the count-based nature of Next-Generation Sequencing (NGS) data challenges these assumptions, prompting the necessity for non-parametric DNA analysis approaches.…”