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However, reliable biomarkers for precision medicine treatment remain elusive due to cellular heterogeneity. Conventional Models based on bulk RNA sequencing and ex vivo assays often fail to capture the intricate molecular pathways and gene networks that underlie treatment response and resistance. Here, NetAML, a novel network\u2010based precision medicine platform that systematically develops 87 drug sensitivity prediction models for 87 clinical drugs using ex vivo drug responses from 520 AML patients with RNA\u2010Seq is presented. The approach leverages network\u2010based analysis and machine learning to identify biologically interpretable gene signatures that capture the complex molecular interactions driving differential drug responses. Notably, the signature genes derived from the models reveal distinct cellular mechanisms. For instance, the co\u2010expression of C19ORF59 and FLT3 is associated with resistance to FLT3 inhibitors. In summary, NetAML offers a powerful strategy for personalized AML treatment by constructing drug\u2010specific models, identifying clinically actionable biomarkers, and supporting the development of optimized, patient\u2010specific therapeutic regimens.<\/jats:p>","DOI":"10.1002\/advs.202506447","type":"journal-article","created":{"date-parts":[[2025,7,11]],"date-time":"2025-07-11T09:33:05Z","timestamp":1752226385000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["A Network\u2010Driven Framework for Drug Response Precision Prediction of Acute Myeloid Leukemia"],"prefix":"10.1002","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0323-5345","authenticated-orcid":false,"given":"Yinyin","family":"Wang","sequence":"first","affiliation":[{"name":"Department of TCMs Pharmaceuticals School of Traditional Chinese Pharmacy China Pharmaceutical University  Nanjing 211198 P. R. 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