Author

Ville O. Paavilainen

University of HelsinkiORCID 0000-0002-3160-7767Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Biochemistry· 1 Bioinformatics· 1 Cell Biology· 1 Genomics· 1

Signature Assays, Organisms & Methods

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Organism:human· 3 Research Focus:cell type annotation· 2 Approach:experimental assay· 2 Assay:proteomics· 2 Assay:bulk RNA-seq· 1 Research Focus:cell signaling & trafficking· 1 Approach:deep learning· 1 Research Focus:differential expression· 1

Frequent Co-Authors

Anja PaateroiD· 2 shared Alexandru DumitrescuiD· 1 shared Anı Akpınar· 1 shared Anna NukariiD· 1 shared Bala Anı AkpınariD· 1 shared Cory D. DunniD· 1 shared Dale TranteriD· 1 shared Eija JokitaloiD· 1 shared

Activity by Year

2020-2026 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 11 mutual co-author links

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2 shared papersCell Biology

Anja PaateroORCID

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4 papers

Recent·Most cited

Solvent specific bioactivities of cone, leaf, and stem extracts from a native Finnish wild hop

Lidija Bitz, Juha‐Matti Pihlava, Pertti Marnila, Lucia Blasco, Ville O. Paavilainen, Merja Hartikainen, Anna Nukari, Dale Tranter, Teija Tenhola-Roininen

This study evaluates the antibacterial, anti-inflammatory, and anticancer activities of solvent-specific extracts from Finnish wild hop tissues.

bioRxiv · Mar 30, 2026 · PreprintBiochemistry

Ribosome-binding protein 1, RRBP1, maintains peroxisomal biogenesis

Kaneez Fatima, Helena Vihinen, Anı Akpınar, Tamara Somborac, Anja Paatero, Eija Jokitalo, Ville O. Paavilainen, Pekka Katajisto, Svetlana A. Konovalova

This study identifies RRBP1 as a novel peroxisome biogenesis factor in human cells, showing it promotes peroxisome formation and stabilizes peroxisomal proteins.

Journal of Cell Science · Dec 15, 2024Peer-reviewedCell Biology

TSignal: A transformer model for signal peptide prediction

Alexandru Dumitrescu, Emmi Jokinen, Anja Paatero, Juho Kellosalo, Ville O. Paavilainen, Harri J Lähdesmäki

TSignal is a transformer model that predicts signal peptides and their cleavage sites, showing improved accuracy across various organisms and peptide types.

Bioinformatics · May 25, 2023Peer-reviewedBioinformatics

A novel phylogenetic analysis and machine learning predict pathogenicity of human mtDNA variants

Bala Anı Akpınar, Paul Carlson, Ville O. Paavilainen, Cory D. Dunn

This study uses phylogenetic analysis and machine learning to predict the pathogenicity of human mtDNA variants, finding many previously uncharacterized variants are likely deleterious.

bioRxiv · Jan 10, 2020 · PreprintGenomics