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December 8, 2025BloodOpen Access

Machine learning uncovers invariant evolutionary molecular trajectories in MDS.

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Authors

ADArda DurmazCBCarlos Bravo‐PérezSPSimona Pagliuca

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Overview

Machine learning uncovers prognostic systems in molecular clusters of MDS, highlighting clinical implications.

Key Points

  • Invariant evolution patterns were identified in molecular clusters associated with MDS progression.
  • The analysis leveraged machine learning to improve prognostic systems based on genetic complexity in 3810 patients.
  • Observational analysis demonstrated distinct molecular clusters that inform the pathways of leukemic evolution.
  • These findings may enhance understanding of disease kinetics, supporting the identification of rapid progression patterns.

Cite This Study

Durmaz et al. (2025) studied this question.

synapsesocial.com/papers/69362f4b4fa91c937236d7b1https://doi.org/10.1182/blood-2025-2060
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1Deciphering clonal progression in MDS via longitudinal sequencing2025
  2. 2Longitudinal genomic and cytogenetic dynamics in myelodysplastic syndromes (MDS): Insights into clonal evolution and disease progression2025
  3. 3Which molecular patterns of high-risk myeloid malignancies are concordantly identified using two machine learning algorithms?2025
  4. 4Molecular subclusters across the continuum of myelodysplastic neoplasms and acute myeloid leukaemia define distinct clinical entities2025
  5. 5Redefining the myeloid disease continuum: Machine learning–based development and validation of an integrated multimodal classification of myeloid neoplasms2025 · 1 citations