Author

Robert Brown

University of California, Los AngelesPrimary ORCID profile (0000-0001-7960-5755 · 1 paper)Unclaimed ORCID profile · follow to get notified of new preprints

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Same name, identified by ORCID

Robert BrowniD1 paper · Imperial College London

Biology Disciplines

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Bioengineering· 1 Bioinformatics· 1 Genetics· 1

Signature Assays, Organisms & Methods

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Organism:human· 3 Assay:bulk RNA-seq· 2 Approach:statistical model· 2 Research Focus:eQTL / molecular QTL mapping· 1 Research Focus:GWAS / association mapping· 1 Assay:GWAS summary statistics· 1 Approach:machine learning (classical)· 1

Frequent Co-Authors

Sriram SankararamaniD· 2 shared Adam LaPrad· 1 shared Bogdan PaşaniuciD· 1 shared Eleazar EskiniD· 1 shared Inon Cohen· 1 shared Jeffrey J. Fredberg· 1 shared Jennifer ZouiD· 1 shared Jinjing Zhou· 1 shared

Activity by Year

2018-2022 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 17 mutual co-author links

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2 shared papersBioinformatics

Sriram SankararamanORCID

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Author profile →

3 papers

Recent·Most cited

Accurate modeling of replication rates in genome-wide association studies by accounting for winner's curse and study-specific heterogeneity

Jennifer Zou, Jinjing Zhou, Sarah Faller, Robert Brown, Sriram Sankararaman, Eleazar Eskin

The paper develops a statistical framework to model replication rates in GWAS by accounting for winner's curse and study-specific heterogeneity, showing significant variation in confounding factors across studies.

G3 Genes Genomes Genetics · Oct 17, 2022Peer-reviewedBioinformatics

Total lung capacity without plethysmography

Ori Adam, Inon Cohen, Wai-Ki Yip, Robert J. Shiner, Peter Calverley, Zachi Peles, Adam LaPrad, Yoni Dagan, Robert Brown, Julian Solway, Jeffrey J. Fredberg

This paper introduces a new method to determine total lung capacity without plethysmography using machine learning on spirometry and flow-interruption data, showing high accuracy and repeatability.

bioRxiv · Aug 30, 2018 · PreprintBioengineering

Haplotype-based eQTL mapping finds evidence for complex gene regulatory regions poorly tagged by marginal SNPs

Robert Brown, Sriram Sankararaman, Bogdan Paşaniuc

The paper introduces a haplotype-based eQTL mapping method that identifies more eGenes than marginal SNP methods, highlighting complex regulatory architectures not captured by single SNPs.

bioRxiv · May 4, 2018 · PreprintGenetics