Author

Alexander G Shanku

Wayne State University

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· 1 Evolutionary Biology· 1 Genomics· 1

Signature Assays, Organisms & Methods

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Approach:statistical model· 2 Assay:ATAC-seq· 1 Research Focus:benchmarking· 1 Assay:bulk RNA-seq· 1 Assay:ChIP-seq· 1 Approach:deep learning· 1 Research Focus:differential expression· 1 Assay:DNA methylation· 1

Frequent Co-Authors

Anthony S FindleyiD· 2 shared Francesca LucaiD· 2 shared Roger Piqué-RegiiD· 2 shared Adnan AlaziziiD· 1 shared Allison L. RichardsiD· 1 shared Andrew D Kern· 1 shared Cristiano PetriniiD· 1 shared Cynthia A. KalitaiD· 1 shared

Activity by Year

2016-2019 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 25 mutual co-author links

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2 shared papersGenomics

Anthony S FindleyORCID

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Author profile →

3 papers

Recent·Most cited

Interpreting coronary artery disease risk through gene-environment interactions in gene regulation

Anthony S Findley, Allison L. Richards, Cristiano Petrini, Adnan Alazizi, Elizabeth R. Doman, Alexander G Shanku, Gordon O. Davis, N. Hauff, Yoram Sorokin, Xiaoquan Wen, Roger Piqué-Regi, Francesca Luca

The paper investigates gene-environment interactions in coronary artery disease by analyzing RNA-seq and ATAC-seq data from endothelial cells exposed to environmental factors, finding links between environmental responses and CAD risk.

Genetics · Sep 6, 2019Peer-reviewedGenomics

circuitSNPs: Predicting genetic effects using a Neural Network to model regulatory modules of DNase-seq footprints

Alexander G Shanku, Anthony S Findley, Cynthia A. Kalita, Heejung Shim, Francesca Luca, Roger Piqué-Regi

circuitSNPs predicts the functional effects of genetic variants on chromatin accessibility using a neural network that models regulatory modules from DNase-seq footprints.

bioRxiv · Jun 3, 2018 · PreprintBioinformatics

Effects of linked selective sweeps on demographic inference and model selection

Daniel R Schrider, Alexander G Shanku, Andrew D Kern

This study evaluates how positive selection affects demographic inference by testing three methods on simulated datasets with linked selective sweeps, finding that selection can bias parameter estimates and lead to incorrect population size inferences.

Genetics · Nov 1, 2016Peer-reviewedEvolutionary Biology