Author

Jun Li

Florida International UniversityORCID 0000-0002-2121-3393Unclaimed ORCID profile · follow to get notified of new preprints

Additional preprints under this name without ORCID

Jun Li17 papers · Peking University

Biology Disciplines

Click to filter author papers

Bioengineering· 1 Bioinformatics· 1 Microbiology· 1

Signature Assays, Organisms & Methods

Click to filter · or open tag

Approach:experimental assay· 2 Research Focus:host-pathogen & immune response· 2 Assay:proteomics· 2 Research Focus:benchmarking· 1 Research Focus:cell signaling & trafficking· 1 Organism:human· 1 Organism:mouse· 1 Research Focus:protein folding & dynamics· 1

Frequent Co-Authors

Amir AsiaeeiD· 1 shared Ehsan IrajizadiD· 1 shared Guodong NiuiD· 1 shared Han LiangiD· 1 shared Hongxu ZhuiD· 1 shared James Patrick LongiD· 1 shared Jiayu LiaoiD· 1 shared Julian RamelowiD· 1 shared

Activity by Year

2025-2026 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 22 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

1 shared paperBioinformatics

Amir AsiaeeORCID

Mutual co-authors in cluster

Author profile →

3 papers

Recent·Most cited

Targeting the AgMOBP1-PfSyn5 mosquito-parasite interface completely blocks malaria transmission

Julian Ramelow, Guodong Niu, Luka Kostallas, Liliana Lai, Xiaohong Wang, Jun Li

This paper identifies AgMOBP1 and PfSyn5 as a critical mosquito-parasite interface for malaria transmission and shows antibodies against PfSyn5 can completely block transmission at low concentrations.

bioRxiv · May 4, 2026 · PreprintMicrobiology

Determine Protein Interaction Affinity without Purification Reveals a Two-enzyme Co-recognition of SUMOylation Substrate using Quantitative FRET (qFRET) Technology

Ling Jiang, Zhehao Xiong, Yan Liu, Runrui Dang, Jun Li, Jiayu Liao

This paper develops a high-throughput qFRET method to determine protein interaction affinity without purification, revealing a two-enzyme co-recognition mechanism for SUMOylation substrates.

bioRxiv · Jul 29, 2025 · PreprintBioengineering

AUC-PR is a More Informative Metric for Assessing the Biological Relevance of In Silico Cellular Perturbation Prediction Models

Hongxu Zhu, Amir Asiaee, Leila Azinfar, Jun Li, Han Liang, Ehsan Irajizad, Kim-Anh Do, James Patrick Long

This study introduces AUC-PR as a better metric for evaluating in silico perturbation models' ability to predict differentially expressed genes, showing it outperforms R2 in capturing biologically relevant outcomes.

bioRxiv · Mar 6, 2025 · PreprintBioinformatics