Author

Aoife Keohane

King's College LondonORCID 0000-0002-4695-4120Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Signature Assays, Organisms & Methods

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Research Focus:differential expression· 2 Organism:human· 2 Approach:Bayesian inference· 1 Assay:bulk RNA-seq· 1 Approach:machine learning (classical)· 1 Assay:proteomics· 1

Frequent Co-Authors

Alzheimer’s Disease Neuroimaging Initiative· 1 shared Amos FolariniD· 1 shared Angela HodgesiD· 1 shared Astrid W. Zobel· 1 shared Caroline JohnstoniD· 1 shared Cerisse GunasingheiD· 1 shared Daniel SoueryiD· 1 shared David Andrew CollieriD· 1 shared

Activity by Year

2017-2018 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 21 mutual co-author links

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1 shared paperBioinformatics

Alzheimer’s Disease Neuroimaging Initiative

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2 papers

Recent·Most cited

NeurosciencebioRxivPreprintMay 21, 2018

Assessing 42 inflammatory markers in 321 control subjects and 887 major depressive disorder cases: BMI and other confounders and overall predictive ability for current depression

Timothy R. Powell, Helena Gaspar, Raymond Chung…·Hong Wang

The paper evaluates 42 inflammatory markers in MDD cases and controls, finding strong confounding effects and limited predictive power of individual markers for MDD diagnosis.

12 citations

humanbulk RNA-seq

𝕏 PostBluesky

BioinformaticsbioRxivPreprintAug 10, 2017

Does genetic risk help to predict amyloid burden in a non-demented population? A Bayesian approach.

Nicola Voyle, Willemijn J. Jansen, Aoife Keohane…·EDAR and DESCRIPA study groups

This study uses Bayesian logistic regression to assess if genetic risk factors predict amyloid burden in non-demented individuals, finding that known factors like age and APOE are more predictive than polygenic risk scores.

1 citation

humanproteomics

𝕏 PostBluesky