Centrality analysis in a drug network and its application to drug repositioning

Keng, Ying Ying; Kwa, Kiam Heong; Ratnavelu, Kuru

doi:10.1016/j.amc.2020.125870

Cited by 5 publications

(3 citation statements)

References 67 publications

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“…Degree centrality represents the number of directly connected neighbours to a node. Centrality analysis, specifically with degree centrality, has been proven to help scientists in disease-related research, such as drug repositioning (18).…”

Section: Key Bacterial Family Differs Between Healthy and T2d Networkmentioning

confidence: 99%

GutMicroNet: an interactive platform for gut microbiome interaction exploration

Arif

Portlock

Güngör³

et al. 2021

Preprint

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The human gut microbiome data has been proven to be a powerful tool to understand the human body in both health and disease conditions. However, understanding their complex interactions and impact on the human body remains a challenging task. Unravelling the species-level interactions could allow us to study the causality of the microbiome. Moreover, it could lead us to better understand the underlying mechanisms of complex diseases and, subsequently, the discovery of new therapeutic targets. Given these challenges and benefits, it has become evident that a freely accessible and centralized platform for presenting gut microbiome interaction is essential to untangle the complexity and open multiple new paths and opportunities in disease- and drug-related research. Here, we present GutMicroNet, an interactive visualization platform of human gut microbiome interaction networks. We generated 45 gut microbiome co-abundance networks from various geographical origins, gender, and diseases based on the data presented in the Human Gut Microbiome Atlas. This interactive platform includes more than 1900 gut microbiome species and allows users to query multiple species at the same time based on their interests and adjust it based on the statistical properties. Moreover, users can download publication-ready figures or network information for further analysis. The platform can be accessed freely on https://gutmicro.net without any login requirements or limitations, including access to the full networks data.

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Section: Key Bacterial Family Differs Between Healthy and T2d Networkmentioning

confidence: 99%

GutMicroNet: an interactive platform for gut microbiome interaction exploration

Arif

Portlock

Güngör³

et al. 2021

Preprint

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“…[13] Centrality measures play a vital role in network analysis, allowing researchers to identify important nodes within a network from a structural perspective. [14] Though frequently used in social network analysis, centrality measures have been adapted as a metric for biological studies since as early as 2001. [15] A previous drug repurposing study ranked drugs by their centrality scores within networks composed of drugs connected based on their side effects and interactions.…”

Section: Introductionmentioning

confidence: 99%

“…[16] Another study suggests that the centralities of drugs in a network of drugs connected based on their side-effect similarities may have signi cant implication in drug repurposing. [14] Most of those published applications mainly leveraged one aspect of drugs, such as side effects or interactions; thus nodes in their established network were speci cally associated with drugs (as opposed to other data types such as diseases, phenotypes, proteins, etc). Inspired by these studies, we proposed to generate integrative rare disease biomedical pro les with heterogenous types of data from our previously developed NCATS Genetic and Rare Diseases (GARD) Knowledge Graph (NGKG) [17] with forty-three rare disease related data resources.…”

Section: Introductionmentioning

confidence: 99%

Integrative Rare Disease Biomedical Profile based Network Supporting Drug Repurposing, a case study of Glioblastoma

McGowan

Sanjak

Mathé

et al. 2023

Preprint

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Background Glioblastoma (GBM) is the most aggressive and common malignant primary brain tumor; however, treatment remains a significant challenge. This study aims to identify drug repurposing candidates for GBM by developing an integrative rare disease profile network containing heterogeneous types of biomedical data.Methods We developed a Glioblastoma-based Biomedical Profile Network (GBPN) by extracting and integrating biomedical information pertinent to GBM-related diseases from the NCATS GARD Knowledge Graph (NGKG). We further clustered the GBPN based on modularity classes which resulted in multiple focused subgraphs, named mc_GBPN. We then identified high-influence nodes by performing network analysis over the mc_GBPN and validated those nodes that could be potential drug repositioning candidates for GBM.Results We developed the GBPN with 1,466 nodes and 107,423 edges and consequently the mc_GBPN with forty-one modularity classes. A list of the ten most influential nodes were identified from the mc_GBPN. These notably include Riluzole, stem cell therapy, cannabidiol, and VK-0214, with proven evidence for treating GBM.Conclusion Our GBM-targeted network analysis allowed us to effectively identify potential candidates for drug repurposing. This could lead to less invasive treatments for glioblastoma while significantly reducing research costs by shortening the drug development timeline. Furthermore, this workflow can be extended to other disease areas.

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