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Using Proteomics Data to Identify Personalized Treatments in Multiple Myeloma: A Machine Learning Approach.

Angeliki Katsenou1,2, Roisin O'Farrell1, Paul Dowling3

  • 1Department of Electronics and Electrical Engineering, Trinity College Dublin, D02 PN40 Dublin, Ireland.

International Journal of Molecular Sciences
|November 14, 2023
PubMed
Summary

Machine learning predicts multiple myeloma (MM) treatment response using proteomic data. This approach shows promise for personalized chemotherapy selection, achieving 81% accuracy in a pilot study.

Keywords:
drug sensitivity scoremachine learningmultiple myelomaproteomics

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Area of Science:

  • Biomedical Informatics
  • Computational Biology
  • Oncology

Background:

  • Multiple myeloma (MM) treatment selection is challenging.
  • Personalized medicine requires identifying patient-specific drug responses.
  • Proteomic profiles offer potential biomarkers for treatment sensitivity.

Purpose of the Study:

  • To develop a machine learning (ML) decision support system for personalized MM treatment.
  • To predict patient sensitivity or resistance to chemotherapeutics based on proteomic data.

Main Methods:

  • Feature selection from proteomic data to identify dominant parameters.
  • Classification algorithms (e.g., Random Forest, SVM) were compared.
  • Data-balancing techniques were explored due to small cohort size.

Main Results:

  • Proteomics data utilization is a promising strategy for MM treatment selection.
  • The ML system achieved an average accuracy of 81% in predicting treatment response.
  • Pilot study demonstrated feasibility despite a small patient cohort (39 patients).

Conclusions:

  • ML-driven analysis of proteomic profiles can guide personalized chemotherapy for MM.
  • Further validation with larger cohorts is warranted to refine ML models.
  • This approach holds significant promise for advancing precision oncology in MM.