Author

Warren D. Taylor

Vanderbilt UniversityORCID 0000-0002-9975-3082Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Signature Assays, Organisms & Methods

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Organism:human· 2 Research Focus:batch correction / integration· 1 Approach:Bayesian inference· 1 Research Focus:benchmarking· 1 Research Focus:database / resource· 1 Approach:experimental assay· 1 Assay:imaging· 1 Approach:machine learning (classical)· 1

Frequent Co-Authors

Adam W. AndersoniD· 1 shared Andrea T. ShaferiD· 1 shared Bennett A. LandmaniD· 1 shared Bramsh Q. ChandioiD· 1 shared Brian D. BoydiD· 1 shared Cailey I. KerleyiD· 1 shared Carmen AndreescuiD· 1 shared Colin B. HanseniD· 1 shared

Activity by Year

2017-2026 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 28 mutual co-author links

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1 shared paperNeuroscience

Adam W. AndersonORCID

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Author profile →

3 papers

Recent·Most cited

Evaluating Clustering and Imputation Approaches for Multivariate Longitudinal Clinical Data Using a Real-World eCDF-Copula Resampling Framework

Hanchang Cai, Ke Xu, Sarrah Osmundson, Olusola Ajilore, Carmen Andreescu, Warren D. Taylor, Jinyuan Liu, Xinlian Zhang

The paper evaluates clustering methods for multivariate longitudinal data using a real-world eCDF-Copula resampling framework to address missing data and improve benchmarking.

bioRxiv · Sep 30, 2026 · PreprintBioinformatics

Pandora: 4-D white matter bundle population-based atlasesderived from diffusion MRI fiber tractography

Colin B. Hansen, Qi Yang, Ilwoo Lyu, François Rheault, Cailey I. Kerley, Bramsh Q. Chandio, Shreyas Fadnavis, Owen A. Williams, Andrea T. Shafer, Susan M. Resnick, David Harold Zald, Laurie E. Cutting, Warren D. Taylor, Brian D. Boyd, Eleftherios Garyfallidis, , Kurt G. Schilling

The paper presents a population-based white matter atlas derived from diffusion MRI fiber tractography, including 216 bundles from 2443 subjects.

Neuroinformatics · Nov 16, 2020Peer-reviewedNeuroscience

Harmonization of cortical thickness measurements across scanners and sites

Jean‐Philippe Fortin, Nicholas C. Cullen, Yvette I. Sheline, Warren D. Taylor, Irem Aselcioglu, Philip A. Cook, Phil Adams, Crystal M. Cooper, Maurizio Fava M. D, Patrick J. McGrath, Melvin G. McInnis, Mary Louise Phillips, Madhukar H. Trivedi, Myrna M. Weissman, Russell Taki Shinohara

ComBat, an empirical Bayes location-and-scale model from genomics, removes additive and multiplicative scanner effects from regional cortical thickness across 11 scanners while preserving age associations.

NeuroImage · Nov 20, 2017Peer-reviewedNeuroscience✦ In-depth digest