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Steps in Outbreak Investigation01:18

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In the ever-evolving field of public health, statistical analysis serves as a cornerstone for understanding and managing disease outbreaks. By leveraging various statistical tools, health professionals can predict potential outbreaks, analyze ongoing situations, and devise effective responses to mitigate impact. For that to happen, there are a few possible stages of the analysis:
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MarkerPredict: predicting clinically relevant predictive biomarkers with machine learning.

Daniel V Veres1,2, Peter Csermely1, Klára Schulc3,4

  • 1Department of Molecular Biology, Semmelweis University, Budapest, Hungary.

NPJ Systems Biology and Applications
|November 21, 2025
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Summary
This summary is machine-generated.

This study introduces MarkerPredict, a tool that uses protein network and disorder data to identify potential predictive biomarkers for targeted cancer therapies. It successfully identified thousands of potential biomarkers, aiding precision oncology.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Oncology

Background:

  • Precision oncology requires predictive biomarkers for effective targeted cancer therapies.
  • Protein network properties and intrinsic disorder influence biomarker potential.

Purpose of the Study:

  • To develop a framework integrating network motifs and protein disorder for predictive biomarker discovery.
  • To create the MarkerPredict tool for identifying potential cancer biomarkers.

Main Methods:

  • Literature-based training sets of protein-interacting pairs were used.
  • Random Forest and XGBoost machine learning models were applied to signalling networks.
  • A Biomarker Probability Score (BPS) was defined based on model ranks.

Main Results:

  • MarkerPredict classified 3670 target-neighbour pairs with high accuracy (0.7-0.96 LOOCV).
  • 2084 potential predictive biomarkers for targeted cancer therapeutics were identified.
  • 426 biomarkers were consistently identified across all calculations, including LCK and ERK1.

Conclusions:

  • The MarkerPredict tool aids in identifying potential predictive biomarkers for targeted cancer therapies.
  • Further validation of high-ranked biomarkers is encouraged for clinical application.
  • MarkerPredict has the potential to impact clinical decision-making in oncology.