This track focuses on the application of artificial intelligence techniques in genomic research. It aims to explore how machine learning can enhance genomic data analysis and interpretation.

This session will highlight the role of data science in advancing proteomic studies. Participants will discuss novel methodologies for analyzing protein interactions and functions using large-scale data.

This track will cover the development and application of bioinformatics tools in systems biology. Emphasis will be placed on integrating multi-omics data to understand biological systems comprehensively.

This session will explore the challenges and solutions associated with big data analytics in the biomedical field. Discussions will include data integration, storage, and analysis techniques for large-scale biological datasets.

This track will focus on the automation of workflows in computational biology to enhance efficiency and reproducibility. Participants will share insights on tools and frameworks that facilitate automated data processing and analysis.

This session will delve into the application of machine learning algorithms for the identification of novel biomarkers. The discussions will cover case studies and methodologies that demonstrate the potential of AI in biomarker research.