Aya Brown Kav

The paper evaluates MAG pipelines using MAG-E, revealing performance gaps in assembly, binning, and quality control for the human gut microbiome.

This paper introduces copangraph, a framework for comparative metagenomic analysis using sequence graphs, showing improved accuracy and computational efficiency in capturing genomic variation across microbial communities.

The paper identifies sample processing errors in microbiome studies using host genetic profiles from metagenomic sequencing and experimental metadata.

DEBIAS-M corrects processing bias in microbiome data, improving cross-study generalization of prediction models by learning interpretable bias-correction factors.

This study predicts preeclampsia using first trimester vaginal microbiome data with machine learning, achieving an auROC of 0.78 in the training set and 0.80 in an independent cohort.