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RanBALL: An Ensemble Machine Learning Framework for Accurate Subtype Identification of Pediatric B-Cell Acute

Lusheng Li1, Hanyu Xiao1, Xinchao Wu1

  • 1Department of Genetics Cell Biology and Anatomy, University of Nebraska Medical Center, Omaha, NE 68198, USA.

Advanced Intelligent Systems (Weinheim an Der Bergstrasse, Germany)
|November 17, 2025
PubMed
Summary
This summary is machine-generated.

RanBALL, a new computational model, accurately identifies B-cell acute lymphoblastic leukemia (B-ALL) subtypes using ensemble random projection. This cost-effective method aids in risk stratification and personalized treatment design for pediatric leukemia.

Keywords:
acute lymphoblastic leukemiaensemble learningmachine learningsubtype identificationtranscriptomic profiling

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Area of Science:

  • Computational biology
  • Pediatric oncology
  • Genomic medicine

Background:

  • B-cell acute lymphoblastic leukemia (B-ALL) is the most common pediatric cancer.
  • Distinct B-ALL subtypes exist, driven by genetic alterations, impacting prognosis.
  • Current subtyping methods are expensive, complex, and time-consuming.

Purpose of the Study:

  • To introduce RanBALL, a novel, accurate, and cost-effective computational model for B-ALL subtyping.
  • To leverage random projection and ensemble learning for improved B-ALL classification.
  • To facilitate risk stratification and personalized therapeutic strategies for B-ALL.

Main Methods:

  • Developed RanBALL, an ensemble random projection-based model.
  • Applied RanBALL to a dataset of over 1700 B-ALL patients.
  • Benchmarked RanBALL against existing methods like ALLSorts and t-SNE.

Main Results:

  • RanBALL achieved high performance metrics: 0.93 accuracy, 0.93 F1-score, and 0.93 Matthews correlation coefficient.
  • Significantly outperformed ALLSorts across all evaluated metrics.
  • Demonstrated superior visualization capabilities compared to t-SNE for B-ALL subtypes.

Conclusions:

  • RanBALL offers an accurate and cost-effective solution for B-ALL subtyping.
  • The model holds potential for discovering subtype-specific markers and therapeutic targets.
  • A Python package for RanBALL is publicly available to promote its adoption and impact.