Author

David A Bennett

Rush 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· 2 Genomics· 1 Neuroscience· 1

Signature Assays, Organisms & Methods

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Organism:human· 3 Approach:statistical model· 3 Research Focus:benchmarking· 2 Approach:Bayesian inference· 1 Assay:bulk RNA-seq· 1 Research Focus:cell type annotation· 1 Approach:deep learning· 1 Research Focus:differential expression· 1

Frequent Co-Authors

A. Ercument Cicek· 1 shared A4 Study team· 1 shared Aaron Topol· 1 shared Adam M. Brickman· 1 shared Albert HofmaniD· 1 shared Albert Smith· 1 shared Alexander TeumeriD· 1 shared Alexander Y. MasloviD· 1 shared

Activity by Year

2016-2026 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 10 mutual co-author links

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1 shared paperGenomics

A. Ercument Cicek

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Author profile →

4 papers

Recent·Most cited

Topographic reference modelling of white matter hyperintensities across the adult lifespan

Mohamad J. Alshikho, Jeffrey D. Pyne, Natalie C. Edwards, Christiane Hale, Dejania Cotton Samuel, Clarissa D. Morales, Natalie Morel, Rafael Vignoli Lippert, Derek B Archer, Michael L. Cuccaro, Arthur W. Toga, Andrew J. Saykin, Shannon L. Risacher, Michael E. Belloy, Walter Kukull, , Adam M. Brickman

This paper introduces vTRM, a framework to characterize WMH spatial patterns across age and sex, revealing distinct associations with aging, vascular, and neurodegenerative processes.

bioRxiv · Nov 3, 2026 · PreprintNeuroscience

Direct probabilistic quantification of mosaic loss of chromosome Y from sequencing data

Jhih-Rong Lin, Yoke‐Chen Chang, Alexander Y. Maslov, Yinghui Song, Tina Gao, Jidong Shan, David A Bennett, Sofiya Milman, Nir Barzilai, Jan Vijg, Cristina Montagna, Zhengdong D. Zhang

This paper introduces BaySeq-Y, a Bayesian method to directly quantify mosaic loss of chromosome Y from sequencing data, showing improved accuracy over existing methods.

bioRxiv · Jul 1, 2026 · PreprintBioinformatics

Gene Expression Elucidates Functional Impact of Polygenic Risk for Schizophrenia

Menachem Fromer, Panos Roussos, Solveig K Sieberts, Jessica S Johnson, David H Kavanagh, Thanneer M Perumal, Douglas M Ruderfer, Edwin C Oh, Aaron Topol, Hardik R Shah, Lambertus L Klei, Robin Kramer, Dalila Pinto, Zeynep H Gumus, A. Ercument Cicek, , Lara M Mangravit

The paper uses gene expression data from schizophrenia cases and controls to show that common variants at 20% of schizophrenia risk loci regulate brain gene expression, with functional impacts on neural progenitor cells and zebrafish neuroanatomy.

bioRxiv · May 9, 2016Genomics

Partial derivatives meta-analysis: pooled analyses when individual participant data cannot be shared

Hieab HH Adams, Hadie Adams, Lenore J Launer, Sudha Seshadri, Reinhold Schmidt, Joshua C Bis, Stephanie Debette, Paul A Nyquist, Jeroen Van der Grond, Thomas H Mosley, Jingyun Yang, Alexander Teumer, Saima Hilal, Gennady V Roshchupkin, Joanna M Wardlaw, , on behalf of the HD-READY Consortium

This paper introduces a meta-analytical method that achieves IPD-level results using only aggregate data, enabling robust analysis when individual participant data cannot be shared.

bioRxiv · Feb 7, 2016 · PreprintBioinformatics