Author

Shuze Wang

University of MichiganORCID 0000-0003-1157-1311Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Signature Assays, Organisms & Methods

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Assay:ChIP-seq· 2 Research Focus:gene regulatory network inference· 2 Organism:human· 2 Approach:statistical model· 2 Approach:deep learning· 1 Research Focus:differential expression· 1 Assay:DNA methylation· 1 Assay:whole-genome sequencing· 1

Frequent Co-Authors

Alan P. BoyleiD· 3 shared Maureen A. SartoriD· 2 shared Raymond G. CavalcanteiD· 2 shared Snehal B. PatiliD· 2 shared Tingting QiniD· 2 shared Chee Khoon LeeiD· 1 shared Christopher Lee· 1 shared Christopher Tai-Yi LeeiD· 1 shared

Activity by Year

2020-2023 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 39 mutual co-author links

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3 shared papersBioinformatics

Alan P. BoyleORCID

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3 papers

Recent·Most cited

BioinformaticsbioRxivPreprintJan 23, 2023

Explain-seq: an end-to-end pipeline from training to interpretation of sequence-based deep learning models

Nanxiang Zhao, Shuze Wang, Qianhui Huang…·Alan P. Boyle

Explain-seq is a pipeline that trains and interprets sequence-based deep learning models to predict enhancer activities and identify transcription factor motifs in genomic data.

DOI 10.1101/2023.01.23.525250

humanDNA methylation

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BioinformaticsbioRxivPreprintOct 22, 2020

Comprehensive enhancer-target gene assignments improve gene set level interpretation of genome-wide regulatory data

Tingting Qin, Christopher Lee, Raymond G. Cavalcante…·Maureen A. Sartor

The paper develops genome-wide enhancer-to-target gene assignments across >500 cell types and shows they outperform naive methods in gene set enrichment analysis.

1 citation

humanChIP-seq

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BioinformaticsNAR Genomics and BioinformaticsPeer-reviewedFeb 6, 2020

Poly-Enrich: Count-based Methods for Gene Set Enrichment Testing with Genomic Regions

Christopher Tai-Yi Lee, Raymond G. Cavalcante, Chee Khoon Lee…·Maureen A. Sartor

Poly-Enrich improves gene set enrichment testing for genomic regions by modeling peak counts with a negative binomial model, outperforming previous methods in certain scenarios.

22 citations

ChIP-seqstatistical model

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