Dr. Susann Bader

Developing CCMpredPy (learning exact statistical models of protein families) and CCMgen (generating realistic synthetic protein alignments). - method development involved data preparation, statistical modeling, model and performance evaluation and customized visualizations - publication in a peer-reviewed scientific journal - software available as Python package on Github - programming in Linux bash, Python, vizualisations with plotly

PhD project in the Soeding Lab: Applying Bayesian modelling and machine learning approaches to predict physical contacts between amino acids in the protein structure from protein sequences alone. - handling the full data science stack: data preparation and cleaning, explorative data analysis, statistical modelling and mathematical optimization, simulation of synthetic data, model and performance evaluation - programming in Linux Bash, Python, R, C++, vizualisations with plotly, ggplot and shiny

I developed and analyzed QSAR models for a biodegradability data set using the Online chemical modeling environment (OCHEM). http://onlinelibrary.wiley.com/doi/10.1002/minf.201300030/full

My goal was to elucidate structure-activity-relationships (SAR) of the glucose transporters SGLT1 and SGLT2 in order to identify selectivity hotspots that can be used to design highly selective inhibitors. http://www.jcheminf.com/content/4/S1/P41

My tasks included the development of an online portal for clients and an internal company Wiki, support in software development and the establishment of automated software tests.

PhD in the Soeding lab: Bayesian Statistical Approach for Prediction of Protein Residue-Residue Contacts

Thesis with Dr. Johannes Soeding: Improving constraints from statistical coupling analysis for de-novo protein structure prediction using amino acid pair propensities

Thesis with Prof. Dr. Ina Koch and Dr. Christian Buning at Sanofi Aventis Deutschland GmbH (grade 1.0): Homologiemodellierung, Struktur-Wirkungsbeziehungen und Selektivitätsanalyse von SGLT Transportern