Yu-Han’s paper on characterizing the obesity metabolome published in International Journal of Obesity

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Yu-Han and colleagues combined untargeted metabolomics with genetics to identify metabolites that may be causes or effects of obesity. They further grouped these metabolites into pathways to highlight how distinct biological mechanisms may be involved in obesity. Their findings demonstrate the strong potential of using untargeted metabolomics and genetically informed causal inference (Mendelian randomization) to uncover causal biological connections between metabolites and various human diseases, especially as larger datasets with both genotype and metabolite profiling data become available in the future. Congratulations on the great work, Yu-Han!

Genoppi is a preprint on bioRxiv

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Our study co-led by @gretapinta, @flassen_, @yuhanhsu and @mjapkim, ‘Genoppi: an open-source software for robust and standardized integration of proteomic and genetic data’, is now posted as a pre-print on bioRxiv. Genoppi allows the seamless integration of proteomic data with genetic information from a multitude of public or custom gene lists to maximize the interpretation of protein interaction datasets.

Cancer network algorithm led by Heiko Horn accepted to ISMB

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The cancer network algorithm paper titled “Prediction of cancer driver genes through network-based moment propagation of mutation scores” has been accepted for presentation at ISMB 2020 and for inclusion in the conference proceedings. This work is in collaboration with Karsten Borgwardt and led by Heiko Horn and Anja Gumpinger.