Cancer remains the leading cause of death worldwide, making research into new drug targets for its treatment essential. In recent years, the development of various tumor-targeted therapies has introduced innovative treatment options. Today, data mining methods in bioinformatics are used to uncover new gene regulatory networks and predict the functional properties of proteins. These methods allow us to identify important biomarkers, genetic variants, and potential therapeutic targets that could be crucial in tumor detection and treatment. High-throughput technologies in genomics, transcriptomics, proteomics, and metabolomics generate vast data, creating an urgent need for efficient data utilization to address biological questions.

This Methods Collection explores the latest advancements and future directions in using computational approaches to decipher cancer heterogeneity, enhance biomarker discovery, and translate findings into clinical practice. It aims to gather multi-omics bioinformatics studies on novel biomarkers and examine how these can be combined with existing cancer strategies, ranging from basic translational science to clinical trials.