Steven Hallam

Cellulase catalysis on cell surfaces using Caulobacter S-layer display

This study uses Caulobacter S-layer display to express functional cellulases for lignocellulosic biomass conversion, demonstrating synergistic catalytic activities.

Methodological Impacts on Microbiome Structure and Indicator Status in the Human Lower Respiratory Tract

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.

A genomic catalog of Earth's bacterial and archaeal symbionts

This study uses machine learning to predict symbiotic lifestyles in microbial genomes, identifying 15-23% of uncultivated microbes as potential symbionts and creating a new catalog of symbiont genomes.

ASPIRE: the Amplicon Sequencing Profiler for Investigating Respiratory Ecosystems

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

Activity-targeted metaproteomics uncovers rare syntrophic bacteria central to anaerobic community metabolism

The paper uses activity-targeted metaproteomics to study rare syntrophic bacteria in anaerobic communities, revealing their metabolic activity and a previously hypothesized oxidative glycine pathway.

Planetary-scale heterotrophic microbial community modeling assesses metabolic synergy and viral impacts

The paper develops a genome-scale metabolic model for ocean microbial communities to infer metabolic processes and viral impacts on biogeochemical cycles.

MetaPathways v3.5: Modularity and Scalability Improvements for Pathway Inference from Environmental Genomes

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.

Relabeling metabolic pathway data with groups to improve prediction outcomes

The paper introduces reMap, a framework for relabeling metabolic pathway data into groups to enhance pathway prediction performance across genome complexity levels.

Modeling the trajectory of SARS-CoV-2 spike protein evolution in continuous latent space using a neural network and Gaussian process

This paper models SARS-CoV-2 spike protein evolution using a neural network and Gaussian process to predict future variants up to five months in advance.

Metabolic pathway prediction using non-negative matrix factorization with improved precision

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

Leveraging Heterogeneous Network Embedding for Metabolic Pathway Prediction

The paper introduces pathway2vec, a software package that uses network embedding to predict metabolic pathways from genomic data, showing improved pathway prediction outcomes.

Metabolic pathway inference using multi-label classification with rich pathway features

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: improved metabolic pathway inference based on active dataset subsampling

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