Cell-cell communication network-based interpretable machine learning predicts cancer patient response to immune checkpoint inhibitors

Immune checkpoint inhibitors (ICIs) have revolutionized cancer treatment. However, only some patients respond to ICIs, and current biomarkers for ICI efficacy have limited performance. Here, we devised an interpretable machine learning (ML) model trained using patient-specific cell-cell communicatio...

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Veröffentlicht in:	Science advances 2024-02, Vol.10 (5), p.eadj0785
Hauptverfasser:	Lee, Juhun, Kim, Donghyo, Kong, JungHo, Ha, Doyeon, Kim, Inhae, Park, Minhyuk, Lee, Kwanghwan, Im, Sin-Hyeog, Kim, Sanguk
Format:	Artikel
Sprache:	eng
Schlagworte:	Biomedicine and Life Sciences Cancer Cell Communication Chemotaxis Humans Immune Checkpoint Inhibitors - pharmacology Immune Checkpoint Inhibitors - therapeutic use Immunology Machine Learning Melanoma SciAdv r-articles
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