Traditional regression models typically assume linear relationships among predictors and response variables, often neglecting complex dependencies and variations across different observations or variables. However, in numerous real-world scenarios, predictors exhibit structured interactions, and their effects on an outcome variable may vary depending on additional covariates. I will introduce novel Bayesian varying-coefficients regression frameworks that employ Gaussian process priors to model non-linear predictor effects and variable selection priors to simultaneously select important predictors and modulating covariates. I will consider extensions to longitudinal data that use two-dimensional Gaussian processes to capture both time-evolving predictor effects and the influence of the covariates on these effects. Using simulation studies, I will show that integrating network information into feature selection improves power to detect the true predictors, also outperforming regularization. I will also show applications to microbiome data.