Junyan Lu @junyanlu.bsky.social · 07/10/2025Across synthetic, semi-synthetic, and experimental serial dilution datasets, msBayesImpute consistently: ✅ Achieved the lowest imputation error ✅ Improved sample-wise normalization ✅ Delivered the highest accuracy in DE analysis across 9 methods 100
Junyan Lu @junyanlu.bsky.social · 07/10/2025Missing values in proteomics are often not missing at random (MNAR). Existing methods either assume MAR or oversimplify MNAR. 💡 msBayesImpute learns protein-specific dropout curves directly from the data using Bayesian matrix factorization + probabilistic dropout models. 120