Author

Cecilia Engel Thomas

Science for Life LaboratoryORCID 0000-0001-6201-6380Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Genetic & Genomic Medicine· 2 Systems Biology· 1

Signature Assays, Organisms & Methods

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Organism:human· 3 Research Focus:differential expression· 2 Assay:proteomics· 2 Approach:statistical model· 2 Research Focus:benchmarking· 1 Assay:bulk RNA-seq· 1 Research Focus:GWAS / association mapping· 1 Assay:GWAS summary statistics· 1

Frequent Co-Authors

Adem Y. DawediD· 2 shared Andrea MariiD· 2 shared Andrew Tym HattersleyiD· 2 shared Angus G. JonesiD· 2 shared Anubha MahajaniD· 2 shared Emmanouil T. DermitzakisiD· 2 shared Ewan R. PearsoniD· 2 shared Giuseppe Nicola GiordanoiD· 2 shared

Activity by Year

2020-2022 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 45 mutual co-author links

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Focal author (center)Selected co-authorORCID co-authorUnlinked name

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2 shared papersGenetic & Genomic Medicine

Adem Y. DawedORCID

Mutual co-authors in cluster

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3 papers

Recent·Most cited

Genetic & Genomic MedicineThe Lancet Diabetes & EndocrinologyPeer-reviewedDec 15, 2022

Pharmacogenomics of GLP-1 Receptor Agonists: A genome-wide analysis of observational data and large randomized controlled trials

Adem Y. Dawed, Andrea Mari, Andrew Anand Brown…·A. Jonsson

This study identifies genetic variants associated with glycaemic response to GLP-1 receptor agonists using observational and trial data, finding variants in GLP-1R and ARRB1 linked to HbA1c reduction.

137 citationstop 1%

humanGWAS summary statistics

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Systems BiologyTranslational OncologyPeer-reviewedJan 13, 2022

Circulating proteins reveal prior use of menopausal hormonal therapy and increased risk of breast cancer

Cecilia Engel Thomas, Leo Dahl, Sanna Byström…·Marike Gabrielson

The paper identifies a proteomic signature associated with menopausal hormonal therapy use and increased breast cancer risk using a data-driven analysis of plasma proteins.

6 citations

humanproteomics

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Genetic & Genomic MedicinePLoS MedicinePeer-reviewedJun 19, 2020

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…·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.

97 citationstop 1%

humanmetabolomics

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