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July 15, 2025

Supplementary Information from A Machine Learning–Based Strategy Predicts Selective and Synergistic Drug Combinations for Relapsed Acute Myeloid Leukemia

Yingjia Chen Liye He Aleksandr Ianevski Kristen Nader Tanja Ruokoranta Nora Linnavirta Juho J. Miettinen Markus Vähä‐Koskela Ida Vänttinen Heikki Kuusanmäki Mika Kontro Kimmo Porkka Krister Wennerberg Caroline A. Heckman Anil K. Giri Tero Aittokallio

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Key Points

Machine learning predicts selective drug combinations for relapsed acute myeloid leukemia.
The approach reveals synergistic effects among various drug pairings, enhancing treatment efficacy.
Data from supplementary results supports the method’s predictive capabilities with detailed analysis.
This advancement could lead to personalized therapy strategies for patients with relapsed acute myeloid leukemia.

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Implication

Machine learning identifies effective drug combinations for relapsed acute myeloid leukemia, suggesting new treatment avenues.

Abstract

Supplementary Tables S1-S3 Supplementary Results Supplementary Discussion

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Cite This Study

Chen et al. (2025) studied this question.

synapsesocial.com/papers/689a02bce6551bb0af8cc64e https://doi.org/10.1158/0008-5472.29567999

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1Supplementary Information from A Machine Learning–Based Strategy Predicts Selective and Synergistic Drug Combinations for Relapsed Acute Myeloid Leukemia2025
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