Author

Luxin Wang

University of California, DavisORCID 0000-0003-4959-6477Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

Click to filter author papers

Microbiology· 2

Signature Assays, Organisms & Methods

Click to filter · or open tag

Approach:deep learning· 2 Organism:bacteria (other)· 1 Research Focus:cell type annotation· 1 Research Focus:differential expression· 1 Research Focus:gene regulatory network inference· 1 Approach:machine learning (classical)· 1 Assay:proteomics· 1 Research Focus:variant effect prediction· 1

Frequent Co-Authors

C. Titus BrowniD· 2 shared Zhuosheng LiuiD· 2 shared Chengchu LiuiD· 1 shared Jiawei Zhang· 1 shared Zhou YiiD· 1 shared Zhuoheng LiiD· 1 shared

Activity by Year

2025-2026 · peak 1/yr

Co-Author Collaboration Network

6 top collaborators · 11 mutual co-author links

Click any collaborator node to inspect or filter shared papers

Focal author (center)Selected co-authorORCID co-authorUnlinked name

Curved chords = mutual co-authorship

2 shared papersMicrobiology

C. Titus BrownORCID

Mutual co-authors in cluster

Author profile →

2 papers

Recent·Most cited

MicrobiologybioRxivPreprintSep 3, 2026

How thermostable direct haemolysin (TDH) diverges from TDH-related haemolysin (TRH)? Reassessing haemolysins in Vibrio parahaemolyticus through functional and structural representation learning

Zhuosheng Liu, Zhou Yi, Chengchu Liu…·Luxin Wang

This study compares TDH and TRH in Vibrio parahaemolyticus using sequence, structure, and function analysis to show their conserved core function with divergent evolutionary paths driven by accumulated mutations.

DOI 10.64898/2026.09.02.748974

bacteria (other)proteomics

𝕏 PostBluesky

MicrobiologybioRxivPreprintApr 8, 2025

Pathogenic potential prediction of Vibrio parahaemolyticus by using pangenome data with high performance machine learning algorithms

Zhuosheng Liu, Zhuoheng Li, Jiawei Zhang…·Luxin Wang

This paper predicts the pathogenic potential of Vibrio parahaemolyticus using pangenome data and machine learning, achieving high AUC scores with Random Forest and Convolutional Neural Network.

DOI 10.1101/2025.04.08.647818

viruswhole-genome sequencing

𝕏 PostBluesky