This track focuses on innovative methodologies and frameworks for life science data modelling. Researchers are invited to present their findings on how these advancements can enhance data representation and interpretation in life sciences.

This session explores the design and implementation of robust big data infrastructures tailored for life science applications. Contributions should address challenges and solutions in managing large-scale biological datasets.

This track examines the development and evaluation of data integration systems that facilitate seamless data sharing across life science domains. Papers should highlight case studies and best practices in integrating heterogeneous data sources.

This session focuses on the establishment and application of data models and standards within life sciences. Contributions should discuss the importance of standardization in improving data interoperability and usability.

This track investigates the use of linked open data principles to enhance data accessibility and connectivity in life sciences. Researchers are encouraged to share their experiences and insights on implementing linked data strategies.

This session highlights the role of machine learning techniques in analyzing and interpreting life science data. Papers should present novel algorithms or applications that demonstrate the potential of machine learning in this field.