Anupriya Tripathi

The paper introduces SIGnature, a framework for scoring gene importance using attributions from scRNA-seq foundation models, and demonstrates its utility in identifying novel associations of the MS1 signature with hyperinflammatory conditions.

This study uses gut microbiome data and machine learning to improve liver disease risk prediction, showing that combining microbiome features with conventional risk factors enhances model performance.

In 5,959 Finns with genotypes and stool metagenomes, GWAS found 583 independent SNP-taxon associations; dairy intake modified LCT effects on Bifidobacterium, and ABO-linked Faecalicatena lactaris tracked secretor status.

EMPress provides an interactive tool for visualizing phylogenetic trees alongside multi-omic data, enabling exploratory analyses of microbiome and metabolome datasets.

The paper maps chemical modifications in an ant fungus garden as plant material passes through, revealing its role in nutrient uptake.

The paper introduces Qemistree, a method for analyzing metabolomics mass spectrometry data using hierarchical molecular fingerprints and chemical ontologies to explore chemical composition.

The paper introduces FBMN, a method for improving molecular networking by enhancing quantitative analysis and isomer distinction using feature detection and alignment tools in the GNPS platform.

This paper expands the interpretability of human metabolomes by using food-based annotations, increasing the fraction of molecular features understood 7-fold.

The paper uses metabolomics to correct metadata classifications in cheetah microbiome studies, revealing antibiotic transfer via grooming and improving understanding of medication effects.

This paper uses untargeted mass spectrometry to analyze chemical changes in foods during preparation and storage, finding that some processes alter molecular composition while others primarily involve extraction.