Author

Ronghua Yang

Northeastern UniversityORCID 0000-0002-9685-7003Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Signature Assays, Organisms & Methods

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Assay:metagenomics· 4 Research Focus:benchmarking· 3 Approach:deep learning· 3 Research Focus:database / resource· 2 Research Focus:gene regulatory network inference· 2 Approach:statistical model· 2 Organism:bacteria (other)· 1 Research Focus:cell type annotation· 1

Frequent Co-Authors

Kang NingiD· 4 shared Haohong Zhang· 3 shared Yuli Zhang· 3 shared Zixin KangiD· 3 shared Chaoqin Liang· 1 shared Haohong ZhangiD· 1 shared Hong BaiiD· 1 shared Jiayun Xiong· 1 shared

Activity by Year

2025-2026 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 26 mutual co-author links

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4 shared papersBioinformatics

Kang NingORCID

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4 papers

Recent·Most cited

MGM2 as a Multimodal Foundation Model for Microbial Community States and Responses

Haohong Zhang, Yuli Zhang, Yuxuan Qi, Tianao Liu, Linfeng Zhao, Ronghua Yang, Kang Ning

The paper introduces MGM2, a multimodal foundation model for microbial communities that predicts responses to interventions and interprets community changes using shared representations of molecular identity and context.

bioRxiv · Sep 30, 2026 · PreprintBioinformatics

Microbial general model: Leveraging large language model for contextualized microbiome analysis

Haohong Zhang, Yuli Zhang, Zixin Kang, Jiayun Xiong, Ronghua Yang, Kang Ning

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

Advanced Science · Jan 25, 2026Peer-reviewedBioinformatics

Uncovering Microbial Biosynthetic Potential with Genomic Context-aware Protein Language Model

Zixin Kang, Haohong Zhang, Chaoqin Liang, Ronghua Yang, Ying Ye, Hong Bai, Yonghui Zhang, Kang Ning

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.

bioRxiv · Apr 29, 2025 · PreprintBioinformatics

Towards a Generative Paradigm for Large-scale Microbiome Analysis by Generative Language Model

Haohong Zhang, Zixin Kang, Yuli Zhang, Ronghua Yang, Kang Ning

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.

bioRxiv · Jan 15, 2025 · PreprintBioinformatics