Author

Lijing Yao

Roche (United States)

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· 4

Signature Assays, Organisms & Methods

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Approach:statistical model· 3 Research Focus:variant calling· 2 Research Focus:benchmarking· 1 Assay:bulk RNA-seq· 1 Approach:deep learning· 1 Assay:DNA methylation· 1 Research Focus:drug response / drug discovery· 1 Approach:experimental assay· 1

Frequent Co-Authors

Hugo Y. K. LamiD· 3 shared Marghoob Mohiyuddin· 2 shared Andreas SchereriD· 1 shared Baitang NingiD· 1 shared Bao Quoc TraniD· 1 shared Bayo LauiD· 1 shared Ben Ernest· 1 shared Benjamin P. BermaniD· 1 shared

Activity by Year

2018-2022 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 29 mutual co-author links

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3 shared papersBioinformatics

Hugo Y. K. LamORCID

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4 papers

Recent·Most cited

Insights from Incorporating Quantum Computing into Drug Design Workflows

Bayo Lau, Prashant S. Emani, Jackson Chapman, Lijing Yao, Tarsus Lam, Paul Willard Merrill, Jonathan Warrell, Mark Gerstein, Hugo Y. K. Lam

This paper introduces HypaCADD, a hybrid classical-quantum workflow for drug design that uses quantum machine learning to predict mutation impacts, showing performance comparable to classical methods.

Bioinformatics · Dec 7, 2022Peer-reviewedBioinformatics

Establishing reference samples for detection of somatic mutations and germline variants with NGS technologies

Li Tai Fang, Bin Zhu, Yongmei Zhao, Wanqiu Chen, Zhaowei Yang, Liz Kerrigan, Kurt J. Langenbach, Maryellen de Mars, Charles Lu, Kenneth B. Idler, Howard J. Jacob, Yuanting Zheng, Luyao Ren, Ying Yu, Erich B. Jaeger, , Paul G. Giresi

The paper establishes reference samples with high-coverage whole-genome sequencing to create truth sets for somatic mutation and germline variant detection using NGS technologies.

Nature Biotechnology · Sep 1, 2021Peer-reviewedBioinformatics

ecTMB: a robust method to estimate and classify tumor mutational burden

Lijing Yao, Yao Fu, Marghoob Mohiyuddin, Hugo Y. K. Lam

This paper introduces ecTMB, a statistical framework for estimating and classifying tumor mutational burden, identifying a new TMB subtype associated with patient survival.

Scientific Reports · Mar 18, 2020Peer-reviewedBioinformatics

ELMER v.2: An R/Bioconductor package to reconstruct gene regulatory networks from DNA methylation and transcriptome profiles

Tiago C. Silva, Simon G. Coetzee, Nicole Gull, Lijing Yao, Dennis J. Hazelett, Houtan Noushmehr, De‐Chen Lin, Benjamin P. Berman

ELMER v.2 reconstructs gene regulatory networks from DNA methylation and transcriptome data to identify Master Regulators in diseases like breast cancer.

Bioinformatics · Oct 25, 2018Peer-reviewedBioinformatics