Data Science

Data science is an interdisciplinary field that aims at extracting knowledge and interpreting results from data. Data scientists require a solid foundation in technical (i.e. programming, maths and engineering), analytical (i.e. statistics and logic) and communication skills. Most proteomic experiments rely on the core elements of data science, namely collecting, cleaning and wrangling large amounts of data. Furthermore, integration of other omics-readouts or additional metadata increases the complexity of knowledge extraction as datasets become heterogeneous and multidimensional. This is especially the case when investigating perturbation experiments, where one or multiple experimental factors are varied in order to investigate and understand their complex molecular effect on living systems.

Proteomics excels at measuring such perturbation experiments as thousands of proteins or modified peptides can be (causally) related to the treatment. However, this also significantly increases the complexity of the data analysis.

Available Projects:

• Phosphorylation motif scoring in drug perturbation data - MA
• Kinase activity benchmarking - MA