Author

Ruili Fang

University of Georgia

Listed by name: these papers carry no ORCID for this author, so different people with the same name may be mixed together.

Biology Disciplines

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Bioinformatics· 2

Signature Assays, Organisms & Methods

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Approach:deep learning· 2 Research Focus:differential expression· 1 Organism:human· 1 Organism:multiple species· 1 Organism:plant (other)· 1 Assay:protein sequence· 1 Research Focus:protein structure prediction· 1 Assay:proteomics· 1

Frequent Co-Authors

Natarajan KannaniD· 2 shared Aarya VenkatiD· 1 shared Anup Prasad· 1 shared Austin Downes· 1 shared Breeanna Rae UrbanowicziD· 1 shared Charles J. Corulli· 1 shared Fei DouiD· 1 shared Kelley W. MoremeniD· 1 shared

Activity by Year

2026-2026 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 31 mutual co-author links

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2 shared papersBioinformatics

Natarajan KannanORCID

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2 papers

Recent·Most cited

CLASPP: A unified model for predicting post-translational modifications

Nathan Gravel, Zhongliang Zhou, Ruili Fang, Austin Downes, Saber Soleymani, Natarajan Kannan

CLASPP is a unified deep learning model that predicts multiple PTM types from protein sequences, addressing data imbalance and improving prediction performance across organisms.

PLoS Computational Biology · Aug 14, 2026Peer-reviewedBioinformatics

Glydentify: An explainable deep learning platform for glycosyltransferase donor substrate prediction

Ruili Fang, Lan Na, Charles J. Corulli, Pradeep Kumar Prabhakar, Steven John Berardinelli, Aarya Venkat, Anup Prasad, Rezwan Mahmud, Kelley W. Moremen, Breeanna Rae Urbanowicz, Fei Dou, Natarajan Kannan

Glydentify predicts donor substrates for glycosyltransferases using deep learning, achieving high accuracy and providing explainable insights into enzyme-substrate relationships.

bioRxiv · Mar 17, 2026 · PreprintBioinformatics