Enhancing Molecular Network‐Based Cancer Driver Gene Prediction Using Machine Learning Approaches: Current Challenges and Opportunities

Journal of Cellular and Molecular Medicine, Journal Year: 2025, Volume and Issue: 29(1)

Published: Jan. 1, 2025

ABSTRACT Cancer is a complex disease driven by mutations in the genes that play critical roles cellular processes. The identification of cancer driver crucial for understanding tumorigenesis, developing targeted therapies and identifying rational drug targets. Experimental validation are time‐consuming costly. Studies have demonstrated interactions among associated with similar phenotypes. Therefore, using molecular network‐based approaches necessary. Molecular random walk‐based approaches, which integrate mutation data protein–protein interaction networks, been widely employed predicting robust predictive potential. However, recent advancements deep learning, particularly graph‐based models, provided novel opportunities enhancing prediction genes. This review aimed to comprehensively explore how machine learning methodologies, network propagation, graph neural autoencoders, embeddings, attention mechanisms, improve scalability interpretability gene prediction.

Language: Английский

DriverMEDS: cancer driver gene identification using mutual exclusivity from embeded features and driver mutation scoring

Methods, Journal Year: 2025, Volume and Issue: unknown

Published: March 1, 2025

Language: Английский

Improving the identification of cancer driver modules using deep features learned from multi-omics data

Computers in Biology and Medicine, Journal Year: 2024, Volume and Issue: 184, P. 109322 - 109322

Published: Nov. 8, 2024

Language: Английский