Integration of machine learning in biomarker discovery for esophageal squamous cell carcinoma: Applications and future directions

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.

Methods

Here, we present a comprehensive overview of ML processes and methodologies that have the potential to advance research and management of esophageal squamous cell carcinoma (ESCC). Specifically, our focus is on their application in key areas such as early detection, prognosis prediction, therapeutic target identification, and drug discovery. Additionally, we examine the challenges and opportunities that ML introduces in the context of ESCC research.

Results

Our findings indicate that ML techniques have the capacity to enhance medical decision-making, improve patient care, and drive progress in healthcare. The prospective integration of ML in oncology poses several challenges, highlighting the need for interdisciplinary collaboration. Addressing these challenges will require coordinated efforts from medical professionals, data scientists, information technology specialists, and policymakers.

Conclusions

The identification of biomarkers for ESCC via ML significantly enhances the quality of medical care and supports expert diagnostic and therapeutic decision-making, thereby markedly improving diagnostic efficiency and advancing the field of intelligent healthcare.