Author

Shibing Deng

Pfizer (United States)ORCID 0000-0002-2867-263XUnclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Signature Assays, Organisms & Methods

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Approach:statistical model· 3 Research Focus:benchmarking· 2 Research Focus:variant effect prediction· 2 Research Focus:cell-cell communication· 1 Assay:CRISPR screen· 1 Research Focus:gene regulatory network inference· 1 Organism:human· 1 Approach:mixed model· 1

Frequent Co-Authors

Anne-Claire KrogeriD· 1 shared Aurélien J. F. DugourdiD· 1 shared Blerta Shtylla· 1 shared Chen Shen· 1 shared Crystal E. Valdez· 1 shared Dénes TüreiiD· 1 shared Diego MañanesiD· 1 shared Donghui HuangiD· 1 shared

Activity by Year

2018-2024 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 24 mutual co-author links

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1 shared paperBioinformatics

Anne-Claire KrogerORCID

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3 papers

Recent·Most cited

Modeling causal signal propagation in multi-omic factor space with COSMOS

Aurélien J. F. Dugourd, Pascal Lafrenz, Diego Mañanes, Víctor Patón, Robin Fallegger, Anne-Claire Kroger, Dénes Türei, Yunfan Bai, Yuxin Li, Michael Trogdon, Drew Nager, Shibing Deng, Chen Shen, John D. Lapek, Blerta Shtylla, Julio Sáez-Rodríguez

The paper introduces COSMOS+ to integrate mechanistic prior knowledge with multi-omics data analysis to generate mechanistic hypotheses about disease resistance mechanisms.

bioRxiv · Jul 15, 2024 · PreprintBioinformatics

Computational correction of cell-specific gene-independent effects in CRISPR-Cas9 essentiality screens: REStricted CUbic SplinEs with Mixed Models (RESCUE-MM)

Julie A. Dias, Shibing Deng, Vinícius Bonato

This paper introduces RESCUE-MM, a one-step method to correct gene-independent effects in CRISPR-Cas9 screens by integrating restricted cubic splines and mixed models, improving accuracy over two-step approaches.

bioRxiv · Oct 22, 2021 · PreprintBioinformatics

TNER: A Novel Background Error Suppression Method for Mutation Detection in Circulating Tumor DNA

Shibing Deng, Maruja Lira, Donghui Huang, Kai Wang, Crystal E. Valdez, Jennifer Kinong, Paul A. Rejto, Jadwiga Renata Bienkowska, James S. Hardwick, Tao Xie

TNER improves mutation detection in ctDNA by reducing background errors, outperforming existing methods on simulated and real data.

BMC Bioinformatics · Oct 20, 2018Peer-reviewedBioinformatics