Ribonucleases of Medical Importance

Silverman, Robert H.

doi:10.1002/0471203076.emm0327

Cited by 2 publications

(4 citation statements)

References 30 publications

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“…Figures 13, 14 depict these networks. For each network we also found the enriched pathways as shown in Tables 11,12,13,14,15,16,17,18,19. The summarized results from the Male-Female (M-F) Differential Network analysis in various tissues reveals significant enrichment of key biological pathways associated with signal transduction, disease mechanisms, and metabolic processes.…”

Section: Differential Network Obtained As M-fmentioning

confidence: 99%

“…The tables 11,12,13,14,15,16,17,18,19 present a comprehensive analysis of KEGG pathway enrichment in different tissues within a male-female (M-F) differential network. This analysis illuminates significant pathways that may underlie gender-specific differences in disease predisposition, progression, and response to treatments, particularly focusing on diabetes comorbidities and cancer.…”

Section: Biological Relevance Of M-fmentioning

confidence: 99%

“…DNA algorithms are usually applied to protein or gene expression networks built by calculating correlations between the expression profiles of gene/protein pairs 16 . Since correlation is not causation, researchers have recently focused on determining a theoretical framework for causality 17,18 .…”

Section: Introductionmentioning

confidence: 99%

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Understanding Complex Systems through DifferentialCausal Networks

Defilippo,

Giorgi,

Veltri

et al. 2024

Preprint

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In the evolving landscape of data science and computational biology,Causal Networks (CNs) have emerged as a robust framework for modelling causal relationships among elements of complex systems derived from experimental data. CNs can efficiently model causal relationships emerging in a single system while comparing multiple systems, allowing to understand rewiring in different cells, tissues, and physiological states with a deeper perspective. Despite the existence of network models, namely differential networks, that have been used to compare coexpression and correlation structures, causality needs to be introduced in differential analysis to robustly provide direction to the edges of such networks, in order to better understand the flows of information, and also to better intervene in their functioning, for example for agricultural or pharmacological purposes. Resolved to reach this ambitious goal, we introduce Differential Causal Networks (DCNs), a novel framework that represents differences between two existing CNs. A DCN is obtained from experimental data by comparing two CNs, and it is a power tool for highlighting differences in causal relations. After a careful definition and design of DCNs, we test our algorithm to model possible differential causal relationships between genesresponsible for the onset of type 2 diabetes mellitus-related pathologies considering patients' sex at the tissue level. DCNs allowed us to shed light on causal differences between sexes across nine tissues. We also compare differences among three possible definitions of DCNs to highlight similarities and differences of biological importance. Code, Data and Supplementary Information are available at https://github.com/hguzzi/DifferentialCausalNetworks.

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Section: Differential Network Obtained As M-fmentioning

confidence: 99%

Section: Biological Relevance Of M-fmentioning

confidence: 99%

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Understanding Complex Systems through DifferentialCausal Networks

Defilippo,

Giorgi,

Veltri

et al. 2024

Preprint

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“…Phenotype prediction aims to predict how an organism or system responds to disease states and perturbations. These methods may be applied at a cellular level to project signaling events and transformations as well as broad phenomena like cell proliferation and survival, but they can also be extended to a network medicine approach, where predictions are made at a patient level to inform diagnosis, prognosis, or treatment response 22,23 .…”

Section: Computational Tasksmentioning

confidence: 99%

A framework for considering prior information in network-based approaches to --omics data analysis

Somers

Fenner

Thirumalaisamy

et al. 2023

Preprint

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For decades, molecular biologists have been uncovering the mechanics of biological systems. Efforts to bring their findings together have led to the development of multiple databases and information systems that capture and present pathway information in a computable network format. Concurrently, the advent of modern omics technologies has empowered researchers to systematically profile cellular processes across different modalities. Numerous algorithms, methodologies, and tools have been developed to use prior knowledge networks in the analysis of omics datasets. Interestingly, it has been repeatedly demonstrated that the source of prior knowledge can greatly impact the results of a given analysis. For these methods to be successful it is paramount that their selection of prior knowledge networks is amenable to the data type and the computational task they aim to accomplish. Here we present a five-level framework that broadly describes network models in terms of their scope, level of detail, and ability to inform causal predictions. To contextualize this framework, we review a handful of network-based omics analysis methods at each level, while also describing the computational tasks they aim to accomplish.

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Ribonucleases of Medical Importance

Cited by 2 publications

References 30 publications

Understanding Complex Systems through DifferentialCausal Networks

Understanding Complex Systems through DifferentialCausal Networks

A framework for considering prior information in network-based approaches to --omics data analysis

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