Author

Eugene Andres Houseman

Oregon State UniversityORCID 0000-0003-0703-1830Unclaimed ORCID profile · follow to get notified of new preprints

Additional preprints under this name without ORCID

Eugene Andres Houseman1 paper

Biology Disciplines

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Bioinformatics· 2 Genomics· 1

Signature Assays, Organisms & Methods

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Approach:statistical model· 3 Research Focus:benchmarking· 2 Research Focus:differential expression· 2 Assay:DNA methylation· 2 Approach:benchmark study· 1 Research Focus:deconvolution· 1 Research Focus:gene regulatory network inference· 1 Research Focus:GWAS / association mapping· 1

Frequent Co-Authors

Aleksandar MilosavljeviciD· 1 shared Andrew E. JaffeiD· 1 shared Andrew Erich TeschendorffiD· 1 shared Arthur Waguet· 1 shared Brock C. ChristenseniD· 1 shared Carmen J. MarsitiD· 1 shared Clémentine DecampsiD· 1 shared Daniel JostiD· 1 shared

Activity by Year

2017-2020 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 14 mutual co-author links

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1 shared paperBioinformatics

Aleksandar MilosavljevicORCID

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3 papers

Recent·Most cited

BioinformaticsBMC BioinformaticsPeer-reviewedJan 13, 2020

Guidelines for cell-type heterogeneity quantification based on a comparative analysis of reference-free DNA methylation deconvolution software

Clémentine Decamps, Florian Privé, Raphael Bacher…·Magali Richard

This study evaluates DNA methylation deconvolution methods for cell-type proportion inference, identifies critical steps for accuracy, and develops a benchmark pipeline.

42 citations

DNA methylationstatistical model

𝕏 PostBluesky

GenomicsbioRxivPreprintJun 7, 2018

Genome-wide abundance of 5-hydroxymethylcytosine in breast tissue reveals unique function in dynamic gene regulation and carcinogenesis

Owen Michael Wilkins, Kevin C. Johnson, Eugene Andres Houseman…·Brock C. Christensen

The paper maps 5-hydroxymethylcytosine (5hmC) distribution in normal breast tissue and links it to gene regulation and cancer prevention.

1 citation

humanDNA methylation

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BioinformaticsbioRxivPreprintMar 28, 2017

Correcting For Cell-Type Heterogeneity In Epigenome-Wide Association Studies: Premature Analyses And Conclusions

Shijie Zheng, Stephan Beck, Andrew E. Jaffe…·Andrew Erich Teschendorff

This paper critiques a study claiming ReFACTor improves EWAS by correcting cell-type heterogeneity, finding its claims lack rigorous statistical support.

1 citation

statistical modelbenchmarking

𝕏 PostBluesky