BVSIMC: Bayesian Variable Selection-Guided Inductive Matrix Completion for Improved and Interpretable Drug Discovery
Sijian Fan, Liyan Xiong, Dayuan Wang, et al.
This paper presents BVSIMC, a machine learning method that helps predict which drugs will work for specific diseases by intelligently selecting the most relevant information (like chemical properties and genetic data) while filtering out noise. The method uses Bayesian statistics to create sparse, interpretable models that not only make better predictions but also reveal which biological features are most important for drug discovery. The researchers demonstrated its effectiveness on real-world problems like predicting drug resistance in tuberculosis and finding new uses for existing drugs.
drug discoverymachine learningBayesian methodsvariable selection