Zixin Kang

The paper introduces MGM, a foundation model for microbiome analysis, showing improved classification and detection of dynamic patterns across diverse contexts.

The paper develops MicroSPEAR, a framework combining a protein language model and microfluidics to discover and validate antimicrobial peptides from isolated habitats.

BGC-Finder uses protein language models to detect and annotate biosynthetic gene clusters in microbial genomes and metagenomes, improving speed and detection of core genes and cytochrome P450s.

This paper introduces MGM 2.0, a generative language model for microbiome analysis that uses sentence-like representations to predict microbial colonization, generate disease-specific profiles, and improve donor selection for fecal microbiota transplantation.