Zhengqiao Zhao

The paper evaluates metagenomic software tools using realistic datasets, highlighting improvements in assembly and taxon profiling while identifying remaining challenges in virus/archaea detection and reproducibility.

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 demonstrates that incremental learning improves metagenomic classification accuracy over time by updating classifiers with new genomic data without retraining from scratch.

This paper introduces a framework for efficient SARS-CoV-2 subtyping using Informative Subtype Markers (ISMs) to track spatiotemporal dynamics of the virus.

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.

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