Data Science in Life Sciences

Development of computational and machine learning algorithms and implementation of databases for knowledge representation and discovery in the life sciences.

Latest developments in high-throughput molecular technologies, imaging, automation and robotics have led to a massive increase in data set sizes in the life sciences thereby shifting the bottle neck from data generation to data analysis. While the large data sets have revealed new biological and chemical insights they have also amplified difficulties in data interpretation.

To extract knowledge from these large datasets, an interdisciplinary approach is required combining biological, chemical and physical knowledge space with computer algorithms, machine learning tools, statistics, databases and IT infrastructure.

The data science team is at the forefront of these developments and is implementing software and modelling solutions for various life science problems including analysis of OMICs data, optimisation of chemical, pharmaceutical production processes and formulation designs, enhancing diagnostic assays and drug development and designing protein molecules with new structures and functions.

Our work supports research labs and institutions, pharma and biotech companies in handling and extracting knowledge from their large data sets and revolutionises their operations by transitioning to data driven research and development.