From Data to Decisions: Machine Learning Transforms Infant Acute Myeloid Leukemia Prognosis

References

[1] Y. Tao, Y. Shen, Y. Tang, H. Shi, L. Wei, and H. You, “Integrating Transcriptomic Profiling and Machine Learning: A Clinically Actionable Prognostic Model for Infant Acute Myeloid Leukemia,” HemaSphere 9, no. 11 (2025): e70251.

[2] H. Bolouri, J. E. Farrar, T. Triche,, et al., “The Molecular Landscape of Pediatric Acute Myeloid Leukemia Reveals Recurrent Structural Alterations and Age-Specific Mutational Interactions,” Nature Medicine 24, no. 1 (2018): 103-112.

[3] S. W. K. Ng, A. Mitchell, J. A. Kennedy, et al., “A 17-gene Stemness Score for Rapid Determination of Risk in Acute Leukaemia,” Nature 540, no. 7633 (2016): 433-437.

[4] T. R. Docking, J. D. K. Parker, M. Jädersten, et al., “A Clinical Transcriptome Approach to Patient Stratification and Therapy Selection in Acute Myeloid Leukemia,” Nature Communications 12, no. 1 (2021): 2474.

[5] J. E. Farrar, J. L. Smith, M. Othus, et al., “Long Noncoding RNA Expression Independently Predicts Outcome in Pediatric Acute Myeloid Leukemia,” Journal of Clinical Oncology 41, no. 16 (2023): 2949-2962.

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2026 The Author(s). MedComm - Future Medicine published by John Wiley & Sons Australia, Ltd on behalf of Sichuan International Medical Exchange & Promotion Association (SCIMEA).