ArticleFrontiers in genetics2024
KNeXT: a NetworkX-based topologically relevant KEGG parser.
Article in Frontiers in genetics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Combined Metabolomic and Transcriptomic Analysis Identifies Metabolic Signatures and Networks Associated with Semen Quality in Geese.Animals : an open access journal from MDPI · 2026Article
- Vitiligo Information Resource database v3.NPJ systems biology and applications · 2026Article
- Integrative single-cell and multi-omics analyses reveal ferroptosis-associated gene expression and immune microenvironment heterogeneity in gastric cancer.Discover oncology · 2025Article
- Influence of multi-species data on gene-disease associations in substance use disorder using random walk with restart models.PloS one · 2025Article
- Spectral divergence prioritizes key classes, genes, and pathways shared between substance use disorders and cardiovascular disease.Frontiers in neuroscience · 2025Article
- A comprehensive analysis of gene expression and the immune landscape in gastric cancer through single-cell and multi-omics approaches.Discover oncology · 2024Article
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Authors and funding
2 authors.
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Abstract
Automating the recreation of gene and mixed gene-compound networks from Kyoto Encyclopedia of Genes and Genomes (KEGG) Markup Language (KGML) files is challenging because the data structure does not preserve the independent or loosely connected neighborhoods in which they were originally derived, referred to here as its topological environment. Identical accession numbers may overlap, causing neighborhoods to artificially collapse based on duplicated identifiers. This causes current parsers to create misleading or erroneous graphical representations when mixed gene networks are converted to gene-only networks. To overcome these challenges we created a python-based KEGG NetworkX Topological (KNeXT) parser that allows users to accurately recapitulate genetic networks and mixed networks from KGML map data. The software, archived as a python package index (PyPI) file to ensure broad application, is designed to ingest KGML files through built-in APIs and dynamically create high-fidelity topological representations. The utilization of NetworkX's framework to generate tab-separated files additionally ensures that KNeXT results may be imported into other graph frameworks and maintain programmatic access to the original
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Registered trials
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