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Despite its utility, prevailing GNEA methodologies are predominantly optimized for binary phenotypes, leading to substantial information loss when applied to continuous biological traits such as drug sensitivity, cancer progression, etc. Additionally, traditional enrichment strategies often exhibit conceptual inconsistency between their null models and test hypotheses, as they rely on permuting phenotype labels rather than genes. To address these limitations, we introduce cell line-specific gene network enrichment analysis (CellGNEA), a computational strategy designed to identify pathway-level molecular interactions associated with continuous phenotypes in a cell line-specific manner. CellGNEA constructs gene regulatory networks tailored to individual cell lines and assesses molecular interplays within these networks by integrating multiple network-derived metrics, including clustering coefficient, PageRank, and regulatory effects. Associations between gene networks and continuous phenotypes are quantified using a Kolmogorov\u2013Smirnov-based statistic, with statistical significance determined via a gene permutation strategy that aligns the null model with the tested hypothesis. Monte Carlo simulation studies indicate that CellGNEA exhibits robustness and enhanced sensitivity in detecting network enrichment linked to continuous phenotypes. Applications of CellGNEA to drug sensitivity-specific gene networks enable the identification of leukemia-related pathways with molecular interactions significantly associated with therapeutic response. Notably, our analysis revealed that imatinib, quizartinib, and ruxolitinib consistently correlate with network-level remodeling across acute myeloid leukemia, myelodysplastic syndrome, and chronic myeloid leukemia pathways, and identified resistance-associated genes including PTPN11, MS4A1, and BTK. Overall, CellGNEA establishes a systematic, scalable framework for functional network analysis of continuous phenotypes, facilitating comprehensive characterization of cell line-specific biological properties and providing valuable insights for systems biology and precision medicine.<\/jats:p>","DOI":"10.1093\/bib\/bbag413","type":"journal-article","created":{"date-parts":[[2026,7,10]],"date-time":"2026-07-10T11:52:46Z","timestamp":1783684366000},"source":"Crossref","is-referenced-by-count":0,"title":["Cell line-specific gene network enrichment analysis for interpreting continuous phenotypes"],"prefix":"10.1093","volume":"27","author":[{"given":"Heewon","family":"Park","sequence":"first","affiliation":[{"name":"School of Mathematics, Statistics and Data Science, Sungshin Women\u2019s University , 2, 34 dagil, Bomun-ro, Seongbuk-gu, Seoul, 02844 ,","place":["Republic of Korea"]},{"name":"M&D Data Science Center, Institute of Integrated Research, Institute of Science Tokyo , 1-5-45 Yushima, Bunkyo-ku, Tokyo 113-8510 ,","place":["Japan"]},{"name":"Human Genome Center, Institute of Medical Science, University of Tokyo , 4-6-1 Shirokanedai, Minato-ku, Tokyo, 108-8639 ,","place":["Japan"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Seiya","family":"Imoto","sequence":"additional","affiliation":[{"name":"Human Genome Center, Institute of Medical Science, University of Tokyo , 4-6-1 Shirokanedai, Minato-ku, Tokyo, 108-8639 ,","place":["Japan"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Satoru","family":"Miyano","sequence":"additional","affiliation":[{"name":"M&D Data Science Center, Institute of Integrated Research, Institute of Science Tokyo , 1-5-45 Yushima, Bunkyo-ku, Tokyo 113-8510 ,","place":["Japan"]},{"name":"Human Genome Center, Institute of Medical Science, University of Tokyo , 4-6-1 Shirokanedai, Minato-ku, Tokyo, 108-8639 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