Author

Heli Julkunen

Finnish Institute for Health and WelfareORCID 0000-0002-4282-0248Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Bioinformatics· 1 Genetic & Genomic Medicine· 1 Genomics· 1

Signature Assays, Organisms & Methods

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Organism:human· 3 Approach:statistical model· 2 Research Focus:differential expression· 1 Research Focus:drug response / drug discovery· 1 Research Focus:GWAS / association mapping· 1 Assay:GWAS summary statistics· 1 Approach:machine learning (classical)· 1 Assay:metabolomics· 1

Frequent Co-Authors

Anna CichońskaiD· 2 shared Peter WürtziD· 2 shared Anastassia KoldeiD· 1 shared Annamari LundqvistiD· 1 shared Courtney J. SmithiD· 1 shared Eric B. FaumaniD· 1 shared Harri T. KoskelaiD· 1 shared Jaanika KronbergiD· 1 shared

Activity by Year

2020-2023 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 22 mutual co-author links

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2 shared papersGenomics

Anna CichońskaORCID

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Author profile →

3 papers

Recent·Most cited

Metabolomic and genomic prediction of common diseases in 477,706 participants in three national biobanks

Nightingale Health Biobank Collaborative Group, Jeffrey C. Barrett, Tõnu Esko, Krista Fischer, Luke Jostins-Dean, Pekka Jousilahti, Heli Julkunen, Tuija Jääskeläinen, Nurlan Kerimov, Sini Kerminen, Anastassia Kolde, Harri T. Koskela, Jaanika Kronberg, Sara Lundgren, Annamari Lundqvist, , Peter Würtz

The paper develops metabolomic risk scores from half a million blood samples to predict 12 common diseases, showing they outperform polygenic scores and benefit from repeat measurements.

medRxiv · Jun 9, 2023 · PreprintGenetic & Genomic Medicine

Integrative analysis of metabolite GWAS illuminates the molecular basis of pleiotropy and genetic correlation

Courtney J. Smith, Nasa Sinnott-Armstrong, Anna Cichońska, Heli Julkunen, Eric B. Fauman, Peter Würtz, Jonathan K. Pritchard

The paper performs metabolite GWAS on UK Biobank data to investigate pleiotropy and genetic correlation, finding pathway-relevant genes and contrasting variant effects.

eLife · Sep 7, 2022Peer-reviewedGenomics

comboFM: leveraging multi-way interactions for systematic prediction of drug combination effects

Heli Julkunen, Anna Cichońska, Prson Gautam, Sándor Szedmák, Jane Douat, Tapio Pahikkala, Tero A. Aittokallio, Juho Rousu

comboFM predicts drug combination effects using machine learning, achieving high accuracy in cancer cell line screening and validating novel synergies.

Nature Communications · Dec 1, 2020Peer-reviewedBioinformatics