Fig. 1: Overview of the study.
From: Decoding Missense Variants by Incorporating Phase Separation via Machine Learning
The upper green panel illustrates PSMutPred, a machine learning approach designed to predict the effect of missense mutations on natural phase separation. Each mutation is converted into a feature vector and distinct models were employed for two main tasks: Identifying mutations that impact PS (termed ‘Impact Prediction’) and determining whether a mutation strengthens or weakens the PS threshold (labeled as ‘Strengthen/Weaken Prediction’). Additionally, PS features, including the output from PSMutPred, were evaluated for their utility in predicting the pathogenicity of missense variants (lower orange panel). dim. dimension.
