Lenwood S. Heath

The paper introduces genomeRxiv, a web server that enables precise strain-level identification of prokaryotes using ANI-based LINs, overcoming limitations of traditional taxonomic classification.

This paper introduces a relaxed topological alignment algorithm to identify conserved regulatory modules across species' gene regulatory networks, demonstrating improved results over strict alignment methods.

The paper uses metagenomic sequencing to analyze 106 symptomatic samples from woody ornamentals in the U.S., identifying 17 high-quality draft genomes of Ceratobasidium sp. associated with vascular streak dieback, revealing genetic differences from related pathogens.

This paper introduces MLG, a graph-based framework for modeling SARS-CoV-2 mutation dynamics, showing its effectiveness in lineage classification and mutation prediction.

This paper introduces a Variant Evolution Graph (VEG) to model SARS-CoV-2 variant evolution using mutational distances and genotypes from GISAID, and infers disease transmission networks.

The paper analyzes genotypic and phenotypic data to show that cool virulence in Ralstonia solanacearum is a quantitative trait not exclusive to R3bv2 strains.

The paper performs a meta-analysis of 100 RSSC genomes to explore evolutionary diversity, identify sub-clades, and evaluate sequevar taxonomy, providing a genome-based framework for classification and identification.

MetaMLP is a machine learning classifier for metagenomic functional profiling that uses word embeddings and a neural network to achieve high accuracy and speed.

EnTrance improves sequence similarity search by using entropy scaling to reduce query times and increase match accuracy compared to existing tools.

This paper introduces a systematic auditing framework to identify biases in machine learning applications in biology, showing that biases can hinder model generalizability and lead to poor performance when training data lacks sufficient signal.

LINbase provides a web service for identifying microbes as members of crowdsourced taxa using genome sequences, enabling collaborative taxonomic classification without sharing full genome data.

ARG-miner is a crowdsourcing-based platform for curating antibiotic resistance genes, achieving high accuracy and efficiency compared to manual curation.

The paper describes a protocol to compare time series transcriptome data between plants using a network module finding algorithm to identify conserved developmental stages.

NanoARG identifies antimicrobial resistance elements from nanopore-derived environmental metagenomes, enabling analysis of ARGs, MRGs, MGEs, and pathogens.

This paper introduces deepARG, a deep learning method for predicting antibiotic resistance genes from metagenomic data, achieving high precision and recall over traditional methods.

The paper introduces a pan-synteny graph method for analyzing multiple prokaryotic genomes, enabling the identification of genomic changes and conservation patterns without a reference genome.