Propel a Digital Culture Shift by Devising Strategies to Better Obtain Data & Augment Its Quality

Time: 10:30 am
day: Pre-Conference Day


AI algorithms, particularly deep learning models, require large and high-quality datasets to make accurate predictions and generate meaningful insights. In drug discovery and preclinical development, obtaining comprehensive and well-annotated datasets can be challenging due to the complexity and diversity of biological systems. Moreover, experimental data can be expensive and time-consuming to generate, leading to limited data availability, especially for rare diseases or specific biological targets.

This workshop will gather experts to discuss

  • Leveraging external data to accelerate sample gathering and attain a better understanding of disease pathogenesis
  • How do we store huge volumes of data while minimizing costs?
  • Increasing open source to improve the quality of data by allowing AI to keep up with the complexity of the biological system
  • Building comparable data sets by developing highly robotic and automized procedures to enable all data sets to be generated in the same way
  • Standardising the approach towards data acquisition and sourcing to enhance data integrity