Author

Amos Folarin

King's College LondonORCID 0000-0002-0333-1927Unclaimed 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:deconvolution· 2 Assay:electronic health records· 2 Organism:human· 2 Approach:statistical model· 2 Approach:Bayesian inference· 1 Research Focus:benchmarking· 1 Research Focus:database / resource· 1 Research Focus:differential expression· 1

Frequent Co-Authors

Richard DobsoniD· 3 shared Angus RobertsiD· 2 shared Asha Agrawal· 2 shared Clive Stringer· 2 shared Honghan WuiD· 2 shared Ismail Emre Kartoglu· 2 shared Richard JacksoniD· 2 shared Robert J. StewartiD· 2 shared

Activity by Year

2017-2018 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 31 mutual co-author links

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3 shared papersBioinformatics

Richard DobsonORCID

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3 papers

Recent·Most cited

BioinformaticsBMC Medical Informatics and Decision MakingPeer-reviewedJun 25, 2018

CogStack - Experiences Of Deploying IntegratedInformation Retrieval And Extraction Services In A Large National Health Service Foundation Trust Hospital

Richard Jackson, Ismail Emre Kartoglu, Asha Agrawal…·Richard Dobson

The paper describes the development and deployment of an open-source information retrieval and extraction system in a UK hospital to improve data flow and governance while supporting clinical and research needs.

134 citationstop 1%

electronic health recordsstatistical model

𝕏 PostBluesky

BioinformaticsJournal of the American Medical Informatics AssociationPeer-reviewedJan 8, 2018

SemEHR: A General-purpose Semantic Search System to Surface Semantic Data from Clinical Notes for Tailored Care, Trial Recruitment and Clinical Research

Honghan Wu, Giulia Toti, Katherine I. Morley…·Richard Dobson

SemEHR is a semantic search system for clinical notes that improves data extraction and patient recruitment in healthcare settings.

123 citationstop 3%

humanelectronic health records

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