Author

Yaoyi Dai

The University of Texas MD Anderson Cancer CenterORCID 0000-0003-1908-2539Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Bioinformatics· 1 Genomics· 1

Signature Assays, Organisms & Methods

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Research Focus:differential expression· 2 Organism:human· 2 Assay:bulk RNA-seq· 1 Approach:deep learning· 1 Assay:DNA methylation· 1 Research Focus:drug response / drug discovery· 1 Approach:machine learning (classical)· 1 Assay:metagenomics· 1

Frequent Co-Authors

Jessica C. LaliD· 2 shared Matthew D. MontierthiD· 2 shared Quang TraniD· 2 shared Ruonan LiiD· 2 shared Scott KopetziD· 2 shared Shuangxi JiiD· 2 shared Wenyi WangiD· 2 shared Aaron Wu· 1 shared

Activity by Year

2024-2026 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 45 mutual co-author links

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2 shared papersBioinformatics

Jessica C. LalORCID

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2 papers

Recent·Most cited

Tumor cell specific total mRNA expression informed neural networks predicts cancer progression

Ankita Paul, Jessica C. Lal, Shuangxi Ji, Carissa Fong, Kevin Chen, Yu Ding, Ruonan Li, Yaoyi Dai, Quang Tran, Matthew D. Montierth, Saverio Alberti, Scott Kopetz, Wenyi Wang

TmSNet predicts tumor cell-specific total mRNA expression from multi-omic data, achieving high accuracy across cancer types and external cohorts.

bioRxiv · May 6, 2026 · PreprintBioinformatics

Subclonal mutation load predicts survival and response to immunotherapy in cancers with low to moderate tumor mutation burden

Yujie Jiang, Matthew D. Montierth, Kaixian Yu, Shuangxi Ji, Quang Tran, Xiaoqian Liu, Jessica C. Lal, Shuai Guo, Aaron Wu, Yaoyi Dai, Seung Jun Shin, Ruonan Li, Shaolong Cao, Yuxin Tang, Tom Lesluyes, , Wenyi Wang

The paper develops CliPP to estimate subclonal mutation load (sML) and shows it predicts survival and immunotherapy response in cancers with low to moderate TMB.

bioRxiv · Jul 3, 2024 · PreprintGenomics