Integration of machine learning in biomarker discovery for esophageal squamous cell carcinoma: Applications and future directions.
Journal:
Pathology, research and practice
Published Date:
Aug 1, 2025
Abstract
PURPOSE: Recent advancements in sequencing technologies and bioinformatics algorithms have facilitated significant breakthroughs in both fundamental and clinical tumor research. Nevertheless, the processing and utilization of large-scale data continue to pose substantial challenges. Machine learning (ML)-based integrative analysis methods present a novel approach for navigating these complex datasets, thereby enhancing the understanding of tumors from multiple perspectives.