NetCrafter: Ontology-derived gene network modeling and interpretation

Authors

Lee, Y., Kim, S., Park, Y., Jeong, E., Jeong, S., Kim, S., Shin, J., Jeong, E., Noh, H., Yoon, S.

Abstract

Understanding the complex nature of multi-functional 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 multi-functional 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 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.

Preprint server: bioRxiv
The authors list and abstract were imported from bioRxiv on 17 Jan 2026.

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