ScholarWorks@숙명여자대학교

초록

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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