Work

A systems-level machine learning approach uncovers therapeutic targets in clear cell renal cell carcinoma.

Year: 2025

Type: article

Abstract: Acknowledgements: This work was supported by the LifeArc grant (RG91966), the National Research Foundation of Korea grant funded by the Ministry of Science and ICT (RS-2025-18362970), a grant from the Korean ARPA-H Project through the Korea Health Industry Development Institute (KHIDI) funded by the Ministry of Health and Welfare (RS-2025-25456722), and the Brain Pool Plus Fellowship Program funded by the Ministry of Science and ICT (RS-2025-25427881). G.Y. was funded by Milner Therapeutics Institute and Cancer Research UK (CRUK). We would like to thank Professor Grant Stewart and Dr James Jones (Department of Surgery, University of Cambridge) for their guidance.

Source: Apollo (University of Cambridge)

Authors: Ruhrberg Estévez, Silas, Baltusyte, Greta, Youssef, Gehad, Han, Namshik

Institution: University of Cambridge

Language: English

Topic: Renal cell carcinoma treatment

Subfield: Pulmonary and Respiratory Medicine

Field: Medicine

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: green