James T. Morton

TM-Vec 2s improves protein remote homology detection by being faster and more accurate than previous methods, enabling efficient large-scale database searches.

The paper presents a Python implementation of ANCOM-BC for differential abundance testing, achieving 100x faster runtime and 87% less memory use compared to the R version.

This paper describes a protocol using MetVAE to construct molecular networks from metabolomics data, identifying lipid correlations in a mouse model.

This study performs a meta-analysis of gut microbiome data across 11 diseases, identifying shared and distinct microbial signatures using machine learning and differential abundance analysis.

The paper introduces a framework using protein language models to learn sequence-structure-function representations and develops a retrieval system for large-scale protein annotation and function prediction.

BIRDMAn is a Bayesian framework for differential abundance analysis in microbiome studies, showing improved statistical power and detecting antibiotic effects and cancer classifiers.

Greengenes2 aligns 16S rRNA and shotgun metagenomic data using a whole-genome phylogeny, showing agreement in principal coordinates, taxonomy, and phenotype effect size.

TM-Vec predicts structural similarity from sequence pairs to detect remote homologs, outperforming sequence alignment methods and matching structure-based alignment performance.

This paper introduces a scalable method using Variational Autoencoders to estimate microbial co-occurrence networks from high-dimensional microbiome data.

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

The paper develops a machine learning workflow for auto-deconvolution of GC-MS data within GNPS, enabling molecular networking and applications in breathomics and volatilome mapping.

The paper introduces DeepBLAST, a method using protein language model embeddings to generate explicit sequence alignments, improving speed and accuracy for structurally similar proteins.

The paper introduces Qurro, a tool for interactively visualizing feature rankings and log-ratios in compositional omics data to improve exploratory analysis.

The paper compares correlation/proportionality methods with MMvec for identifying microbe-metabolite interactions and finds MMvec performs better on real data due to its handling of sparsity.

MetaRiPPquest identifies RiPPs by combining genome/metagenome mining with mass spectrometry analysis, discovering 5 novel natural products from 6 datasets.