The Impact of Pathway Database Choice on Statistical Enrichment Analysis and Predictive Modeling

Mubeen, Sarah; Hoyt, Charles Tapley; Gemünd, André; Hofmann‐Apitius, Martin; Fröhlich, Holger; Domingo‐Fernándéz, Daniel

doi:10.3389/fgene.2019.01203

Cited by 94 publications

(83 citation statements)

References 63 publications

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“…protein–protein interactions, complexes) ( 51 ). This information is integrated into PathwayForte ( 52 ) and the feedback from these analyses led to additional curation efforts on WikiPathways including renaming of pathways.…”

Section: Connections To Other Initiativesmentioning

confidence: 99%

WikiPathways: connecting communities

Martens

Ammar

Riutta

et al. 2020

Nucleic Acids Research

667

502

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WikiPathways (https://www.wikipathways.org) is a biological pathway database known for its collaborative nature and open science approaches. With the core idea of the scientific community developing and curating biological knowledge in pathway models, WikiPathways lowers all barriers for accessing and using its content. Increasingly more content creators, initiatives, projects and tools have started using WikiPathways. Central in this growth and increased use of WikiPathways are the various communities that focus on particular subsets of molecular pathways such as for rare diseases and lipid metabolism. Knowledge from published pathway figures helps prioritize pathway development, using optical character and named entity recognition. We show the growth of WikiPathways over the last three years, highlight the new communities and collaborations of pathway authors and curators, and describe various technologies to connect to external resources and initiatives. The road toward a sustainable, community-driven pathway database goes through integration with other resources such as Wikidata and allowing more use, curation and redistribution of WikiPathways content.

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Section: Connections To Other Initiativesmentioning

confidence: 99%

WikiPathways: connecting communities

Martens

Ammar

Riutta

et al. 2020

Nucleic Acids Research

667

502

View full text Add to dashboard Cite

show abstract

“…For instance, mapping of the protein targets of drugs either to the same or orthogonal pathways may help to reveal the MoA of both multi-targeted monotherapies and combination therapies. However, various databases may contain different representations of the same biological pathways, which leads to variable results of statistical target pathway enrichment analysis and predictive models in the context of precision medicine [54]. In this section, we highlight six pathway databases that contain information of compound target pathways, along with their characteristics in terms of the number of proteins, compounds, pathways and interactions (Table 3).…”

Section: Biological Pathway Information Resourcesmentioning

confidence: 99%

Artificial intelligence, machine learning, and drug repurposing in cancer

Tanoli

Vähä‐Koskela

Aittokallio

2021

Expert Opinion on Drug Discovery

107

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Introduction: Drug repurposing provides a cost-effective strategy to re-use approved drugs for new medical indications. Several machine learning (ML) and artificial intelligence (AI) approaches have been developed for systematic identification of drug repurposing leads based on big data resources, hence further accelerating and de-risking the drug development process by computational means. Areas covered: The authors focus on supervised ML and AI methods that make use of publicly available databases and information resources. While most of the example applications are in the field of anticancer drug therapies, the methods and resources reviewed are widely applicable also to other indications including COVID-19 treatment. A particular emphasis is placed on the use of comprehensive target activity profiles that enable a systematic repurposing process by extending the target profile of drugs to include potent off-targets with therapeutic potential for a new indication. Expert opinion: The scarcity of clinical patient data and the current focus on genetic aberrations as primary drug targets may limit the performance of anticancer drug repurposing approaches that rely solely on genomics-based information. Functional testing of cancer patient cells exposed to a large number of targeted therapies and their combinations provides an additional source of repurposing information for tissue-aware AI approaches.

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“…The gene set enrichment analysis highlighted cytokine, interleukin, and toll-like receptor signalling pathways that are involved in regulating various aspects of innate and adaptive immune responses 97 . Such results may be compromised when other pathway databases such as KEGG 98 and WikiPathways 99 are employed, as the relevant pathways and molecular interactions in the pathways are different from Reactome 100 , 101 . To circumvent this issue, one possibility could be to extract the overlapped networks between the different databases of pathways; however, this is not always possible due to the differences in annotation of genes and interactions.…”

Section: Discussionmentioning

confidence: 99%

Network- and systems-based re-engineering of dendritic cells with non-coding RNAs for cancer immunotherapy

et al. 2021

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Dendritic cells (DCs) are professional antigen-presenting cells that induce and regulate adaptive immunity by presenting antigens to T cells. Due to their coordinative role in adaptive immune responses, DCs have been used as cell-based therapeutic vaccination against cancer. The capacity of DCs to induce a therapeutic immune response can be enhanced by re-wiring of cellular signalling pathways with microRNAs (miRNAs). Methods: Since the activation and maturation of DCs is controlled by an interconnected signalling network, we deploy an approach that combines RNA sequencing data and systems biology methods to delineate miRNA-based strategies that enhance DC-elicited immune responses. Results: Through RNA sequencing of IKKβ-matured DCs that are currently being tested in a clinical trial on therapeutic anti-cancer vaccination, we identified 44 differentially expressed miRNAs. According to a network analysis, most of these miRNAs regulate targets that are linked to immune pathways, such as cytokine and interleukin signalling. We employed a network topology-oriented scoring model to rank the miRNAs, analysed their impact on immunogenic potency of DCs, and identified dozens of promising miRNA candidates, with miR-15a and miR-16 as the top ones. The results of our analysis are presented in a database that constitutes a tool to identify DC-relevant miRNA-gene interactions with therapeutic potential ( https://www.synmirapy.net/dc-optimization ). Conclusions: Our approach enables the systematic analysis and identification of functional miRNA-gene interactions that can be experimentally tested for improving DC immunogenic potency.

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The Impact of Pathway Database Choice on Statistical Enrichment Analysis and Predictive Modeling

Cited by 94 publications

References 63 publications

WikiPathways: connecting communities

WikiPathways: connecting communities

Artificial intelligence, machine learning, and drug repurposing in cancer

Network- and systems-based re-engineering of dendritic cells with non-coding RNAs for cancer immunotherapy

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