Prashant Singh

The paper introduces a Gaussian random field model for imputing missing data in longitudinal microbiome studies, achieving comparable accuracy to baselines while modeling temporal and multivariate correlations.

The paper predicts food allergies and postpartum depression using microbiome time series data with imputation and logarithmic transformation, achieving balanced accuracies of 0.72 and 0.62 respectively.

The paper develops a computational pipeline to evaluate inference accuracy across different model levels and parameter inference settings for negative feedback gene regulation.