Haohong Zhang

The paper introduces NK-RCPP, a workflow for prioritizing neurokinin-receptor peptide candidates using receptor-matched scores and structural-neighbour comparisons.

PaleoRigor uses a language model with constraints to process paleomicrobiome data, ensuring reproducibility and preventing invalid operations.

MetaClaw is an auditable AI agent that performs end-to-end metagenomic and multi-omics analysis, recovering key biological markers across multiple studies.

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

This study develops the Tumor Microbiome Survival Index (TMSI) to predict cancer patient survival and drug sensitivity across multiple cancer types based on intra-tumor microbiome data.

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 BGC-Prophet, a transformer-based model for identifying and extrapolating biosynthetic gene clusters (BGCs) in microbial genomes and metagenomes, revealing evolutionary patterns and enhancing BGC discovery.

This study develops ASD-cancer, a deep learning framework that identifies survival subtypes in cancers by integrating tumor microbiome and transcriptome data, revealing microbial-gene interactions linked to patient outcomes.