Author

Femke Rutters

Amsterdam Public HealthORCID 0000-0002-1008-678XUnclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Genetic & Genomic Medicine· 2

Signature Assays, Organisms & Methods

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Assay:bulk RNA-seq· 2 Organism:human· 2 Assay:metabolomics· 2 Assay:proteomics· 2 Research Focus:benchmarking· 1 Research Focus:differential expression· 1 Research Focus:gene regulatory network inference· 1 Research Focus:GWAS / association mapping· 1

Frequent Co-Authors

Adem Y. DawediD· 2 shared Alison J. HeggieiD· 2 shared Andrea MariiD· 2 shared Andrew Tym HattersleyiD· 2 shared Angus G. JonesiD· 2 shared Anubha MahajaniD· 2 shared Donna McEvoyiD· 2 shared Federico De MasiiD· 2 shared

Activity by Year

2020-2023 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 45 mutual co-author links

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2 shared papersGenetic & Genomic Medicine

Adem Y. DawedORCID

Mutual co-authors in cluster

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2 papers

Recent·Most cited

Genetic analysis of blood molecular phenotypes reveals regulatory networks affecting complex traits: a DIRECT study.

Andrew Anand Brown, Juan J. Fernandez-Tajes, Mun‐Gwan Hong, Caroline Brorsson, Robert Wilhelm Koivula, David Davtian, Théo Dupuis, Ambra Sartori, Theodora-Dafni Michalettou, Ian M. Forgie, Jonathan Adam, Kristine Højgaard Allin, Robert J. Caiazzo, Henna Cederberg, Federico De Masi, , Ulrik Plesner Jacobsen

The paper identifies shared genetic regulation across mRNA, proteins, and metabolites in blood, revealing networks and mediators of GWAS variants.

Nature Communications · Aug 21, 2023Peer-reviewedGenetic & Genomic Medicine

Predicting and elucidating the etiology of fatty liver disease using a machine learning-based approach: an IMI DIRECT study

Naeimeh Atabaki‐Pasdar, Mattias Ohlsson, Ana Viñuela, Francesca Frau, Hugo Pomares‐Millan, Mark Haid, Angus G. Jones, Elizabeth Louise Thomas, Robert Wilhelm Koivula, Azra Kurbasic, Pascal M. Mutie, Hugo Fitipaldi, Juan Fernandez, Adem Y. Dawed, Giuseppe Nicola Giordano, , Paul W. Franks

This study develops machine learning models to predict liver fat content using multi-omic and clinical data from NAFLD patients, achieving higher accuracy than existing tools.

PLoS Medicine · Jun 19, 2020Peer-reviewedGenetic & Genomic Medicine