The life science sector is increasingly relying on modern data analysis for interpreting complex structures and converting research findings into feasible results. Modern statistical procedures such as machine learning are particularly applicable in this regard and are being used in many areas of life science.

Statistical analysis is beneficial to numerous practical applications:

In bioinformatics, genome analysis is of crucial importance. It is used to detect new genes, indicate splice sites and determine regulatory connections.

In the field of cheminformatics, machine learning procedures are being used to predict the characteristics of chemical substances in silico without first needing to be synthesised. This is particularly useful in the development of medicinal agents.

Specific statistical methods can enhance the data quality of EEG recordings and other medical analyses. An improved signal-to-noise ratio greatly aids practitioners in their diagnoses.