ArticleBriefings in bioinformatics2026
NetCrafter: ontology-derived gene network modeling and functional interpretation.
Article in Briefings in bioinformatics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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10 authors.
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Abstract
Understanding the complex nature of multifunctional interactions among genes is crucial for interpreting omics data. We developed NetCrafter, an ontology-driven platform for constructing de novo gene networks that are specific to each input gene list and quantitatively defined by ontology-weighted similarity. By incorporating the probabilistic association of ontology or curated gene sets into a weighted Tanimoto similarity metric, NetCrafter transforms enrichment results into quantitative semantic similarity scores between genes, enabling the creation of context-specific statistical networks. These networks can be further decomposed into optimal sub-networks, facilitating multifunctional interpretation and the identification of gene interaction hotspots. NetCrafter also supports the integration of heterogeneous omics-derived gene lists through consensus ontology scoring. Importantly, this list-specific, quantitative framework reveals functional hotspots and target-biomarker relationships-even in cases where ontology terms alone are not predictive of node-level attributes such as clustered regularly interspaced short palindromic repeats (CRISPR) efficacy. NetCrafter provides an interactive platform for constructing and interpreting dynamic, context-specific gene networks, leveraging ontology-based functional associations to uncover underlying mechanisms and identify key nodes. It is freely available at https://netcrafter.sookmyung.ac.kr and integrated into Q-omics platform (https://qomics.ai) to enhance the utility of cancer omics data.
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