Author

Yang Cao

Sichuan UniversityORCID 0000-0002-1925-2123Unclaimed ORCID profile · follow to get notified of new preprints

Additional preprints under this name without ORCID

Yang Cao1 paper · University of Cambridge

Biology Disciplines

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Bioinformatics· 3

Signature Assays, Organisms & Methods

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Research Focus:benchmarking· 2 Research Focus:drug response / drug discovery· 2 Approach:statistical model· 2 Approach:benchmark study· 1 Research Focus:database / resource· 1 Research Focus:differential expression· 1 Research Focus:gene regulatory network inference· 1 Approach:machine learning (classical)· 1

Frequent Co-Authors

Binxing Fang· 1 shared Boning ZhangiD· 1 shared Chelsea ShuiD· 1 shared Chuanyi Liu· 1 shared Collin J. TokheimiD· 1 shared Dian LiiD· 1 shared Eric S. FischeriD· 1 shared Fang YeiD· 1 shared

Activity by Year

2020-2026 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 18 mutual co-author links

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1 shared paperBioinformatics

Binxing Fang

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3 papers

Recent·Most cited

A mechanism-annotated benchmark reveals limited fidelity to drug-response signatures in single-cell perturbation models

Lehang Li, Shaoming Duan, Xinyu Zha, Fang Ye, Yuhao Zhang, Xinyi Zhang, Yang Cao, Chuanyi Liu, Binxing Fang

This paper introduces a benchmark to evaluate single-cell drug perturbation models by assessing their fidelity to drug-response signatures, finding that expression similarity is a poor proxy for mechanism preservation.

bioRxiv · Aug 26, 2026 · PreprintBioinformatics

Machine learning modeling of protein-intrinsic features predicts tractability of targeted protein degradation

Wubing Zhang, Shourya S. Roy Burman, Jiaye Chen, Katherine A. Donovan, Yang Cao, Chelsea Shu, Boning Zhang, Zexian Zeng, Shengqing Stan Gu, Yi Zhang, Dian Li, Eric S. Fischer, Collin J. Tokheim, X. Shirley Liu

The paper develops a machine learning model to predict protein degradability for targeted protein degradation, identifying key features and extending predictions to the proteome.

Genomics Proteomics & Bioinformatics · Oct 1, 2022Peer-reviewedBioinformatics

Classification of multigene families of African swine fever viruses

Zhaozhong Zhu, Huiting Chen, Yang Cao, Taijiao Jiang, Yuanqiang Zou, Yousong Peng

This study classifies MGF proteins of ASFV into 35 groups based on sequence homology and provides a web server for classification.

bioRxiv · Feb 20, 2020 · PreprintBioinformatics