Author

Yue Cao

Texas A&M UniversityORCID 0000-0002-5949-3175Unclaimed ORCID profile · follow to get notified of new preprints

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Yue Cao4 papers · Fudan University

Biology Disciplines

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Bioinformatics· 3

Signature Assays, Organisms & Methods

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Approach:deep learning· 2 Assay:protein sequence· 2 Research Focus:protein structure prediction· 2 Research Focus:benchmarking· 1 Research Focus:differential expression· 1 Research Focus:gene regulatory network inference· 1 Organism:human· 1 Approach:machine learning (classical)· 1

Frequent Co-Authors

Yang SheniD· 3 shared Mostafa KarimiiD· 2 shared Haoran Chen· 1 shared Oluwaseyi Moronfoye· 1 shared Shaowen ZhuiD· 1 shared Yuanfei SuniD· 1 shared

Activity by Year

2019-2021 · peak 1/yr

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6 top collaborators · 12 mutual co-author links

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3 shared papersBioinformatics

Yang ShenORCID

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3 papers

Recent·Most cited

TALE: Transformer-based protein function Annotation with joint sequence-Label Embedding

Yue Cao, Yang Shen

TALE is a deep learning model for protein function annotation that uses joint sequence-label embeddings to improve generalizability to novel sequences, species, and functions.

Bioinformatics · Mar 22, 2021Peer-reviewedBioinformatics

De Novo Protein Design for Novel Folds using Guided Conditional Wasserstein Generative Adversarial Networks (gcWGAN)

Mostafa Karimi, Shaowen Zhu, Yue Cao, Yang Shen

This paper develops a deep generative model, gcWGAN, to design novel protein folds using sequence-structure data, achieving higher fold coverage and sequence diversity than a competing method.

Journal of Chemical Information and Modeling · Sep 18, 2020Peer-reviewedBioinformatics

Predicting Pathogenicity of Missense Variants with Weakly Supervised Regression

Yue Cao, Yuanfei Sun, Mostafa Karimi, Haoran Chen, Oluwaseyi Moronfoye, Yang Shen

This paper introduces a weakly supervised regression model to predict pathogenicity of missense variants and infer molecular mechanisms from imprecise annotations.

Human Mutation · May 30, 2019Peer-reviewedBioinformatics