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Machine Learning Models for the Identification of Prognostic and Predictive Cancer Biomarkers: A Systematic Review.

Qasem Al-Tashi¹, Maliazurina B Saad¹, Amgad Muneer¹

¹Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.

International Journal of Molecular Sciences

|May 13, 2023

Summary

This summary is machine-generated.

Distinguishing predictive biomarkers, which guide treatment, from prognostic biomarkers, which predict outcomes, is vital in personalized medicine. This review analyzes recent advancements in biomarker identification methods, particularly machine learning, to improve accuracy and patient care.

Keywords:

biomarker discovery deep learning feature selection machine learning personalized medicine predictive biomarker prognostic biomarker subgroup identification

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Area of Science:

Biomedical Science
Oncology
Computational Biology

Background:

Biomarker identification is essential for personalized medicine, enabling tailored clinical and research strategies.
Differentiating predictive biomarkers (treatment response) from prognostic biomarkers (outcome prediction) is challenging due to overlapping functions.
Misclassification can lead to significant financial and personal consequences for patients.

Conclusions:

Accurate biomarker identification is crucial for effective personalized medicine and cancer treatment.
Advanced statistical and machine learning approaches are key to overcoming classification challenges.
Further research is needed to address existing obstacles and explore new avenues in biomarker discovery.