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

Journal of Cellular and Molecular Medicine, Год журнала: 2025, Номер 29(1)

Опубликована: Янв. 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.

Язык: Английский

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

Methods, Год журнала: 2025, Номер unknown

Опубликована: Март 1, 2025

Язык: Английский

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

Computers in Biology and Medicine, Год журнала: 2024, Номер 184, С. 109322 - 109322

Опубликована: Ноя. 8, 2024

Язык: Английский