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Here, we present a network medicine-based machine learning framework that integrates single-cell transcriptomics, bulk multi-omic profiles, genome-wide CRISPR perturbation screens, and protein-protein interaction networks to systematically prioritise disease-specific targets. Applied to clear cell renal cell carcinoma, the framework successfully recovered established targets and predicted five therapeutic candidates, with subsequent in vitro validation demonstrating that among these,\n                    <jats:italic>ENO2<\/jats:italic>\n                    inhibition had the strongest anti-tumour effect, followed by\n                    <jats:italic>LRRK2<\/jats:italic>\n                    , a repurposing candidate with phase III Parkinson\u2019s disease inhibitors. The proposed approach advances target discovery by moving beyond single-feature, single-modality heuristics to a scalable, machine learning-driven strategy that is generalisable across diseases.\n                  <\/jats:p>","DOI":"10.1038\/s42004-026-02049-9","type":"journal-article","created":{"date-parts":[[2026,5,6]],"date-time":"2026-05-06T09:43:20Z","timestamp":1778060600000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A network medicine framework for multi-modal data integration in therapeutic target discovery"],"prefix":"10.1038","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-9957-4479","authenticated-orcid":false,"given":"Greta","family":"Baltu\u0161yt\u0117","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-1121-3863","authenticated-orcid":false,"given":"Isaac J. 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