Satabdi Saha

The paper evaluates common beta diversity metrics in microbiome analysis, identifies their mathematical shortcomings, and introduces a classification framework with tools to improve their validity.

The paper introduces a Bayesian ensemble method for multi-omics prediction and classification, showing improved performance and uncertainty quantification across four datasets.

This study benchmarks Bayesian and frequentist methods for differential gene expression analysis in dose-response scRNA-seq studies, finding that multi-group methods outperform two-group approaches.

The paper proposes a multiview graph learning method to reconstruct gene regulatory networks from single-cell RNA sequencing data, showing improved performance on synthetic and real datasets.

scSGL infers gene regulatory networks from single-cell RNA sequencing data by learning signed graphs that capture both activating and inhibitory gene relationships, while handling cell-cycle heterogeneity and dropouts.