ArticleBMC bioinformatics2019
PathMe: merging and exploring mechanistic pathway knowledge.
Article in BMC bioinformatics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
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Who cites it
25 citing papers in PubMed.
- Ten quick tips for causal analysis of biomedical omics data.PLoS computational biology · 2026Article
- Pathway Analysis Interpretation in the Multi-Omic Era.Biotech (Basel (Switzerland)) · 2025Review
- PathDIP 5: improving coverage and making enrichment analysis more biologically meaningful.Nucleic acids research · 2024Article
- Applications of multi-omics analysis in human diseases.MedComm · 2023Review
- On the correspondence between the transcriptomic response of a compound and its effects on its targets.BMC bioinformatics · 2023Article
- Benchmarking causal reasoning algorithms for gene expression-based compound mechanism of action analysis.BMC bioinformatics · 2023Article
- Inflammation modulates intercellular adhesion and mechanotransduction in human epidermis via ROCK2.iScience · 2023Article
- PathIN: an integrated tool for the visualization of pathway interaction networks.Computational and structural biotechnology journal · 2023Article
- Survey for Computer-Aided Tools and Databases in Metabolomics.Metabolites · 2022Review
- Exploring the Complex Network of Heme-Triggered Effects on the Blood Coagulation System.Journal of clinical medicine · 2022Article
- Contexts and contradictions: a roadmap for computational drug repurposing with knowledge inference.Briefings in bioinformatics · 2022Review
- Computational analyses of mechanism of action (MoA): data, methods and integration.RSC chemical biology · 2022Review
- OncoboxPD: human 51 672 molecular pathways database with tools for activity calculating and visualization.Computational and structural biotechnology journal · 2022Article
- CLEP: a hybrid data- and knowledge-driven framework for generating patient representations.Bioinformatics (Oxford, England) · 2021Article
- Pathway analysis in metabolomics: Recommendations for the use of over-representation analysis.PLoS computational biology · 2021Article
- The status of causality in biological databases: data resources and data retrieval possibilities to support logical modeling.Briefings in bioinformatics · 2021Article
- COVID-19 Knowledge Graph: a computable, multi-modal, cause-and-effect knowledge model of COVID-19 pathophysiology.Bioinformatics (Oxford, England) · 2021Article
- A method for the rational selection of drug repurposing candidates from multimodal knowledge harmonization.Scientific reports · 2021Article
- Integrated intra- and intercellular signaling knowledge for multicellular omics analysis.Molecular systems biology · 2021Article
- WikiPathways: connecting communities.Nucleic acids research · 2021Article
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5 authors.
Funding
Abstract
backgroundThe complexity of representing biological systems is compounded by an ever-expanding body of knowledge emerging from multi-omics experiments. A number of pathway databases have facilitated pathway-centric approaches that assist in the interpretation of molecular signatures yielded by these experiments. However, the lack of interoperability between pathway databases has hindered the ability to harmonize these resources and to exploit their consolidated knowledge. Such a unification of pathway knowledge is imperative in enhancing the comprehension and modeling of biological abstractions.
resultsHere, we present PathMe, a Python package that transforms pathway knowledge from three major pathway databases into a unified abstraction using Biological Expression Language as the pivotal, integrative schema. PathMe is complemented by a novel web application (freely available at https://pathme.scai.fraunhofer.de/ ) which allows users to comprehensively explore pathway crosstalk and compare areas of consensus and discrepancies.
conclusionsThis work has harmonized three major pathway databases and transformed them into a unified schema in order to gain a holistic picture of pathway knowledge. We demonstrate the utility of the PathMe framework in: i) integrating pathway landscapes at the database level, ii) comparing the degree of consensus at the pathway level, and iii) exploring pathway crosstalk and investigating consensus at the molecular level.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.