Author

Shaoyu Li

University of North Carolina at CharlotteORCID 0000-0003-1687-619XUnclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Genetics· 1 Genomics· 1 Neuroscience· 1

Signature Assays, Organisms & Methods

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Approach:statistical model· 3 Assay:bulk RNA-seq· 2 Research Focus:differential expression· 2 Organism:human· 2 Approach:experimental assay· 1 Research Focus:gene regulatory network inference· 1 Research Focus:GWAS / association mapping· 1 Assay:GWAS summary statistics· 1

Frequent Co-Authors

Arthur G. HuntiD· 1 shared ASHWINI SHIVAKUMAR· 1 shared Carli Camporeale· 1 shared Cassandra Catacalos-GoadiD· 1 shared Christoph DieterichiD· 1 shared Daniel A Janies· 1 shared Dennis W. DicksoniD· 1 shared Doaa HassaniD· 1 shared

Activity by Year

2018-2026 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 24 mutual co-author links

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1 shared paperGenomics

Arthur G. HuntORCID

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3 papers

Recent·Most cited

GenomicsRNA BiologyPeer-reviewedOct 28, 2026

Nucleotide-resolution Mapping of RNA N6-Methyladenosine (m6A) modifications and comprehensive analysis of global polyadenylation events in mRNA 3' end processing in malaria pathogen Plasmodium falciparum

Cassandra Catacalos-Goad, Manohar Chakrabarti, Doaa Hassan…·Kausik Chakrabarti

The paper maps m6A modifications and polyadenylation events in Plasmodium falciparum mRNAs, identifying stage-specific m6A sites and their potential role in 3' end processing.

2 citations

humanmouse

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NeuroscienceMolecular NeurodegenerationPeer-reviewedJul 13, 2020

Deciphering cellular transcriptional alterations in Alzheimer's disease brains

Xue Wang, Mariet Allen, Shaoyu Li…·Nilüfer Ertekin‐Taner

The paper identifies cell-intrinsic differentially expressed genes in Alzheimer's disease by analyzing bulk-RNAseq data and comparing results to single nucleus RNAseq.

68 citations

humanbulk RNA-seq

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GeneticsGenetic EpidemiologyPeer-reviewedNov 19, 2018

An optimal kernel-based method for gene set association analysis

Tao He, Shaoyu Li, Ping‐Shou Zhong, Yuehua Cui

The paper introduces an efficient kernel-based method for gene set association analysis that controls type I error and achieves high power in high-dimensional settings.

11 citations

GWAS summary statisticsstatistical model

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