Manuel Seefelder
Manuel Seefelder is a postdoctoral researcher in the Department of Gene Therapy at Ulm University Hospital, Germany. His background is in molecular medicine, with a doctorate on the huntingtin-associated protein 40 and its role in Huntington's disease. His current work sits at the intersection of wet-lab research, proteomics and computational method development: he builds Bayesian and deep-learning pipelines for analyzing protein interactome data from mass spectrometry experiments. Julia is his primary research language, and BayesInteractomics.jl grew directly out of the need to rigorously quantify interaction evidence in his own experiments. He also developed ProteinCoLoc, a Bayesian tool for colocalization analysis in fluorescence microscopy (Scientific Reports, 2024), and teaches a workshop on applied Bayesian statistics for PhD students at Ulm University.
Session
Identifying genuine protein-protein interactions from mass spectrometry data requires disentangling real biology from experimental noise. BayesInteractomics.jl tackles this by fitting three complementary Bayesian models (detection, enrichment, and dose-response) and combining their evidence through copula mixture models. Built on RxInfer.jl and Copulas.jl, it leverages Julia's type system, multiple dispatch, and threading to analyze thousands of proteins in minutes.