Soft Computing Approaches to Extract Biologically Significant Gene Network Modules

Roy, Swarup; Manners, Hazel Nicolette; Jha, Monica; Guzzi, Pietro Hiram; Kalita, Jugal

doi:10.1007/978-981-10-7455-4_3

Cited by 7 publications

(5 citation statements)

References 27 publications

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“…In biological systems analysis, it is pertinent to note that while nodes represent directly quantifiable entities, the derivation of edges necessitates observing a sequence of temporal data. For instance, gene networks originating from microarray experiments necessitate the inference of edges from data through statistical graphical models [19][20][21].…”

Section: Related Workmentioning

confidence: 99%

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

Guzzi,

Arkaprava,

Milano

et al. 2024

Preprint

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The rewiring of molecular interactions in various conditions leads to distinct phenotypic outcomes. Differential Network Analysis (DNA) is dedicated to exploring these rewirings within gene and protein networks. Leveraging statistical learning and graph theory, DNA algorithms scrutinize alterations in interaction patterns derived from experimental data. Introducing a novel approach to differential network analysis, we incorporate differential gene expression based on sex and gender attributes. We hypothesize that gene expression can be accurately represented through non-Gaussian processes. Our methodology involves quantifying changes in non-parametric correlations among gene pairs and expression levels of individual genes. Applying our method to public expression datasets concerning diabetes mellitus and atherosclerosis in liver tissue, we identify gender-specific differential networks. Results underscore the biological relevance of our approach in uncovering meaningful molecular distinctions.

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Section: Related Workmentioning

confidence: 99%

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

Guzzi,

Arkaprava,

Milano

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…When dealing with biological systems it should be noted, that nodes are directly measurable, while edges among them should be derived from a set of observations over time. For instance, when considering gene networks derived from micro-array experiments, nodes are fixed while edges should be inferred from the observations by means of statistical graphical models (Lauritzen Lauritzen;Roy et al 2018;Galicia et al 2020). In a statistical graphical model, we use a graph G = (V , E) , and each node v ∈ V is associated with a set of m random variables X 1 , .…”

Section: Dna Algorithmsmentioning

confidence: 99%

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

et al. 2023

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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.

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“…When dealing with biological systems it should be noted, that nodes are directly measurable, while edges among them should be derived from a set of observations over time. For instance, when considering gene networks derived from micro-array experiments, nodes are fixed while edges should be inferred from the observations by means of statistical graphical models [32,33,34]. In a statistical graphical model, we use a graph G = (V, E), and each node v ∈ V is associated with a set of m random variables X 1 , .…”

Section: Related Workmentioning

confidence: 99%

Differential network analysis between sex of thegenes related to comorbidities of type 2 mellitusdiabetes.

Guzzi

Cortese

Mannino

et al. 2023

Preprint

View full text Add to dashboard Cite

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 represent the set of association 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 difference in the insurgence of comorbidities between males and females. Conclusion Main contributions of this work are: (i) the definition of a novel framework of analysis able to shed out light on the differences betweeen males and females; (ii) the identification of differential networks related to diabetes comorbidities. Availability and implementation: All the datasets underlying this article are publicly available. Data and source code can be accessed through the web at https://github.com/hguzzi/DifferentialNetworkDiabetes.

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Soft Computing Approaches to Extract Biologically Significant Gene Network Modules

Cited by 7 publications

References 27 publications

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

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

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

Differential network analysis between sex of thegenes related to comorbidities of type 2 mellitusdiabetes.

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