De Novo Pathway Enrichment with KeyPathwayMiner

Alcaraz, Nicolás; Hartebrodt, Anne; List, Markus

doi:10.1007/978-1-4939-9873-9_14

Cited by 3 publications

(8 citation statements)

References 19 publications

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“…Additionally, we have a mapping Z : V → {0,1} m from each node v ∈ V to its corresponding row in the matrix A . The objective is to extract a subnetwork G ′ = ( V ′, E ′) ⊂ G such that G ′ is connected and the set of nodes V ′ satisfy the constraints of the INES or the GloNE strategy ( Alcaraz et al, 2020 , p. 183-184).…”

Section: Methodsmentioning

confidence: 99%

“…Formally, this can be described in the following way: Given a biological network G = ( V , E ) , an indicator matrix A n , m , K node exceptions, L case exceptions and s being a sample from the indicator matrix extract maximally connected subnetworks G ′ = ( V ′, E ′) for which following formula evaluates to true for all nodes v ∈ V ′ but K exceptions:

( Alcaraz et al, 2020 , p. 183).…”

Section: Methodsmentioning

confidence: 99%

“…The strategy can be formally defined as follows ( Alcaraz et al, 2020 , p. 184): Given a biological network G = ( V , E ), an indicator matrix A n , m , L case exceptions and s being a sample from the indicator matrix extract maximally connected subnetworks G ′ = ( V ′, E ′) for which the following formula evaluates to true:

…”

Section: Methodsmentioning

confidence: 99%

“…

; The process is repeated until no more nodes are left which could be added to gain a valid solution ( Alcaraz et al, 2020 , p. 184-185).…”

Section: Methodsmentioning

confidence: 99%

See 3 more Smart Citations

KeyPathwayMineR: De Novo Pathway Enrichment in the R Ecosystem

et al. 2022

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De novo pathway enrichment is a systems biology approach in which OMICS data are projected onto a molecular interaction network to identify subnetworks representing condition-specific functional modules and molecular pathways. Compared to classical pathway enrichment analysis methods, de novo pathway enrichment is not limited to predefined lists of pathways from (curated) databases and thus particularly suited for discovering novel disease mechanisms. While several tools have been proposed for pathway enrichment, the integration of de novo pathway enrichment in end-to-end OMICS analysis workflows in the R programming language is currently limited to a single tool. To close this gap, we have implemented an R package KeyPathwayMineR (KPM-R). The package extends the features and usability of existing versions of KeyPathwayMiner by leveraging the power, flexibility and versatility of R and by providing various novel functionalities for performing data preparation, visualization, and comparison. In addition, thanks to its interoperability with a plethora of existing R packages in e.g., Bioconductor, CRAN, and GitHub, KPM-R allows carrying out the initial preparation of the datasets and to meaningfully interpret the extracted subnetworks. To demonstrate the package’s potential, KPM-R was applied to bulk RNA-Seq data of nasopharyngeal swabs from SARS-CoV-2 infected individuals, and on single cell RNA-Seq data of aging mice tissue from the Tabula Muris Senis atlas.

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Section: Methodsmentioning

confidence: 99%

( Alcaraz et al, 2020 , p. 183).…”

Section: Methodsmentioning

confidence: 99%

…”

Section: Methodsmentioning

confidence: 99%

“…

; The process is repeated until no more nodes are left which could be added to gain a valid solution ( Alcaraz et al, 2020 , p. 184-185).…”

Section: Methodsmentioning

confidence: 99%

See 2 more Smart Citations

KeyPathwayMineR: De Novo Pathway Enrichment in the R Ecosystem

et al. 2022

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“…KeyPathwayMiner includes a set of heuristic algorithms (e.g., AI strategy ant colony optimization) and exact (fixed-parameter) methods for such integration. For a detailed explanation of KeyPathwayMiner algorithms and functionality, please refer to [ 130 , 136 ].…”

Section: How To Steer Biological Network Into the Right Directionmentioning

confidence: 99%

Alveolar Regeneration in COVID-19 Patients: A Network Perspective

Gupta¹,

Srivastava²,

Minocha

et al. 2021

IJMS

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A viral infection involves entry and replication of viral nucleic acid in a host organism, subsequently leading to biochemical and structural alterations in the host cell. In the case of SARS-CoV-2 viral infection, over-activation of the host immune system may lead to lung damage. Albeit the regeneration and fibrotic repair processes being the two protective host responses, prolonged injury may lead to excessive fibrosis, a pathological state that can result in lung collapse. In this review, we discuss regeneration and fibrosis processes in response to SARS-CoV-2 and provide our viewpoint on the triggering of alveolar regeneration in coronavirus disease 2019 (COVID-19) patients.

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Transcriptomic and Drug Discovery Analyses Reveal Natural Compounds Targeting the KDM4 Subfamily as Promising Adjuvant Treatments in Cancer

et al. 2022

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KDM4 proteins are a subfamily of histone demethylases that target the trimethylation of lysines 9 and 36 of histone H3, which are associated with transcriptional repression and elongation respectively. Their deregulation in cancer may lead to chromatin structure alteration and transcriptional defects that could promote malignancy. Despite that KDM4 proteins are promising drug targets in cancer therapy, only a few drugs have been described as inhibitors of these enzymes, while studies on natural compounds as possible inhibitors are still needed. Natural compounds are a major source of biologically active substances and many are known to target epigenetic processes such as DNA methylation and histone deacetylation, making them a rich source for the discovery of new histone demethylase inhibitors. Here, using transcriptomic analyses we determined that the KDM4 family is deregulated and associated with a poor prognosis in multiple neoplastic tissues. Also, by molecular docking and molecular dynamics approaches, we screened the COCONUT database to search for inhibitors of natural origin compared to FDA-approved drugs and DrugBank databases. We found that molecules from natural products presented the best scores in the FRED docking analysis. Molecules with sugars, aromatic rings, and the presence of OH or O- groups favor the interaction with the active site of KDM4 subfamily proteins. Finally, we integrated a protein-protein interaction network to correlate data from transcriptomic analysis and docking screenings to propose FDA-approved drugs that could be used as multitarget therapies or in combination with the potential natural inhibitors of KDM4 enzymes. This study highlights the relevance of the KDM4 family in cancer and proposes natural compounds that could be used as potential therapies.

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De Novo Pathway Enrichment with KeyPathwayMiner

Cited by 3 publications

References 19 publications

KeyPathwayMineR: De Novo Pathway Enrichment in the R Ecosystem

KeyPathwayMineR: De Novo Pathway Enrichment in the R Ecosystem

Alveolar Regeneration in COVID-19 Patients: A Network Perspective

Transcriptomic and Drug Discovery Analyses Reveal Natural Compounds Targeting the KDM4 Subfamily as Promising Adjuvant Treatments in Cancer

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