Ryan J. McLaughlin

This study evaluates how respiratory tract sampling methods affect inferred microbiome structure and identifies bronchial brushing as yielding a distinct but low-biomass microbial signal.

ASPIRE processes and analyzes linked ASV-VOC data from respiratory microbiome studies to enable scalable comparative analysis and standardized file outputs.

The paper improves the MO_SCPLOWETAC_SCPLOWPO_SCPLOWATHWAYSC_SCPLOW pipeline for pathway inference from environmental genomes, enhancing usability and performance through code refactoring and algorithm updates.

This paper introduces triUMPF, a triple non-negative matrix factorization method for metabolic pathway inference, achieving improved precision on multi-organismal datasets.

The paper introduces mlLGPR, a multi-label logistic regression tool for metabolic pathway prediction using rich pathway features, and benchmarks its performance across diverse datasets.

leADS improves metabolic pathway inference by using active dataset subsampling to address class imbalance in training data.