Skip to content

Author

Zhong Peng

2 papers indexed here

We haven’t gathered this author’s papers yet. Follow them and we’ll fetch their work.

Not the right person? Other researchers publish under this name.

Open access Jul 2026

MicroWorldOmics: All-in-one Desktop Solution for Microbiome Profiling, Virome Analysis, and Unexplored "Dark Matter" Discovery.

The large amount of high-throughput sequencing data generated in ecology, medicine, and pharmacology has increased the complexity of data analysis and interpretation. However, the microbiome and virome fields still lack a user-friendly and programming-free desktop application for comprehensive analysis of microbiome and virome data, with a particular gap in virome analysis and "dark matter" exploration. To address this gap, we introduce MicroWorldOmics, a plugin-based desktop application designed to offer a streamlined one-stop solution for life sciences and biomedical research. Its plugin-based architecture allows users to analyze data interactively and in parallel, simplifying tasks that typically require advanced bioinformatics skills. MicroWorldOmics is a comprehensive software suite tailored for microbiome and virome research, featuring 92 sub-applications across four main modules: epidemiology analysis, in-depth metagenomic/amplicon and virome profiling, and "dark matter" exploration. MicroWorldOmics leverages over 80 Python modules and 600 R packages for diverse bioinformatics, statistics, deep learning, and visualization tasks, accommodating multiple input and output formats including GFF3, FASTA, CSV, PNG, JPG, JSON, and TXT. To enhance user productivity, the software is compatible with Windows, Linux, and macOS systems, and includes demo data for easy benchmarking. In summary, MicroWorldOmics is intended to facilitate microbiome and virome data analysis for life sciences and biomedicine researchers without a programming background. It is available at https://hzaurzli.github.io/.

Runze Li, Wei Dong, Zhuang Yang et al. · 0 citations
#artificial intelligence Preprint Jun 2026

DeepTCM1.0: A Multi-Expert AI Agent for Deciphering Mechanisms of Chinese Herbal Formulae Based on General Large Language Models

The DeepTCM1.0 framework was applied to the mechanistic interpretation of Guizhi Decoction from the dual perspectives of classical traditional Chinese medicine theory and modern scientific research, enabling systematic and interpretable mechanistic analysis of TCM compound formulas.

Wenxin Duan, Hanwei Wang, Zhong Peng et al. · 0 citations