Himel Mallick

HuMMANet harmonizes gut microbiome and metabolome data across 46 studies to identify reproducible metabolite-microbiome associations and their clinical relevance.

This study identifies microbial species and metabolic pathways associated with immune checkpoint inhibitor response in melanoma through a meta-analysis of seven microbiome cohorts.

This paper introduces Coracle, a conformalized framework for multimodal AI that provides valid uncertainty estimates, outperforming existing methods in uncertainty estimation and calibration.

MaAsLin 3 fits hurdle models separating microbial prevalence (augmented logistic) and abundance (median-adjusted log-linear) associations, improving precision over existing differential abundance tools and showing 77% of IBD associations are prevalence-driven.

The paper introduces an open-source ecosystem for microbiome data science using Bioconductor, supporting modular workflows for analyzing complex, multi-modal datasets.

This paper introduces BayesCOOP, a Bayesian method for multimodal integration that avoids MCMC and variational approaches, showing improved performance in estimation and uncertainty assessment.

This study uses multi-omics analysis to identify key biological changes in ASD children after microbiota transplant therapy, showing shifts in microbiome, metabolites, and symptoms.

The paper introduces an ensemble framework for differential analysis that combines multiple models to improve performance across various omics data types.

This paper introduces Robseq, a statistical model for RNA-Seq differential expression that addresses FDR inflation caused by group heteroscedasticity, showing improved control of false discovery and type-I error rates.

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

The paper introduces Tweedie models for differential expression analysis in scRNA-seq, showing improved performance over existing methods across platforms.

The paper introduces a statistical model to simulate microbial community profiles and evaluates its performance in benchmarking analysis methods.

The paper develops MMUPHin for meta-analysis of microbial communities in IBD, identifying consistent microbial associations and showing no discrete microbiome types.