Stephen Woloszynek

The paper introduces a deep learning model for microbial DNA sequence analysis, achieving phenotypic prediction and sequence visualization with performance comparable to conventional methods.

The paper introduces a topic modeling approach to analyze 16S rRNA amplicon data, linking microbial co-occurrence patterns to functional and environmental features.

The paper develops sequence embeddings for 16S rRNA data to create numeric representations suitable for machine learning, showing they preserve biological information and perform well in downstream tasks.

Deep learning rivals or beats baselines across imaging, health records, genomics and drug discovery but is not yet transformative; scarce labels, privacy and opacity remain hurdles.

This paper evaluates a topic model approach for analyzing microbiome data, showing it captures dynamic behavior and identifies gut configurations in time-series data.

Biomod2EZ provides R scripts to enhance Biomod2 by generating reports, adding annotations, and offering tutorials for ecological niche modeling.