Author

Jianzhao Gao

Nankai UniversityORCID 0000-0002-9943-4786Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Bioinformatics· 2

Signature Assays, Organisms & Methods

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Research Focus:benchmarking· 1 Approach:deep learning· 1 Approach:graph neural network· 1 Approach:machine learning (classical)· 1 Assay:protein sequence· 1 Assay:protein structure· 1 Research Focus:protein structure prediction· 1

Frequent Co-Authors

A. Keith DunkeriD· 1 shared Ágnes TantosiD· 1 shared Alexander Miguel MonzóniD· 1 shared Alok K. SharmaiD· 1 shared Anastasia ChasapiiD· 1 shared András HatosiD· 1 shared Andrey V. KajavaiD· 1 shared Arne ElofssoniD· 1 shared

Activity by Year

2021-2023 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 45 mutual co-author links

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1 shared paperBioinformatics

A. Keith DunkerORCID

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2 papers

Recent·Most cited

Identifying B-cell epitopes using AlphaFold2 predicted structures and pretrained language model

Yuansong Zeng, Zhuoyi Wei, Qianmu Yuan, Sheng Chen, Weijiang Yu, Yutong Lu, Jianzhao Gao, Yuedong Yang

GraphBepi predicts B-cell epitopes using AlphaFold2 structures and a pretrained language model, outperforming existing methods in AUC and AUPR.

Bioinformatics · Apr 1, 2023Peer-reviewedBioinformatics

Critical Assessment of Protein Intrinsic Disorder Prediction

Marco Necci, Damiano Piovesan, CAID Predictors, Md Tamjidul Hoque, Ian Walsh, Sumaiya Iqbal, Michele Vendruscolo, Pietro Sormanni, Chen Wang, Daniele Raimondi, Ronesh Sharma, Yaoqi Zhou, Thomas Litfin, Oxana V. Galzitskaya, Michail Yu. Lobanov, , Silvio C. E. Tosatto

The paper evaluates 43 methods for predicting intrinsically disordered regions and binding regions in proteins, finding that deep learning methods outperform older physicochemical approaches but binding prediction remains challenging.

Nature Methods · Apr 19, 2021Peer-reviewedBioinformatics