Author

V. Abrishami

Listed by name: these papers carry no ORCID for this author, so different people with the same name may be mixed together.

Biology Disciplines

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Biochemistry· 1 Microbiology· 1 Pharmacology & Toxicology· 1

Signature Assays, Organisms & Methods

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Research Focus:drug response / drug discovery· 2 Approach:experimental assay· 2 Organism:bacteria (other)· 1 Research Focus:cell type annotation· 1 Research Focus:chromatin & epigenetic regulation· 1 Research Focus:clustering· 1 Approach:deep learning· 1 Research Focus:differential expression· 1

Frequent Co-Authors

J. T. Huiskonen· 2 shared A. Diez-Sanchez· 1 shared A. Jennings· 1 shared A. Quilez-Alvarez· 1 shared C. J. Kirk· 1 shared C. Kalyanaraman· 1 shared D. Jimenez-Carretero· 1 shared D. McMinn· 1 shared

Activity by Year

2018-2022 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 19 mutual co-author links

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2 shared papersBiochemistry

J. T. Huiskonen

Mutual co-authors in cluster

Author profile →

3 papers

Recent·Most cited

Signal peptide mimicry primes Sec61 for client-selective inhibition

S. Rehan, D. Tranter, P. P. Sharp, E. Lowe, J. Anderl, T. Muchamuel, V. Abrishami, S. Kuivanen, N. Wenzell, A. Jennings, C. Kalyanaraman, G. B. Craven, T. Strandin, M. Javanainen, O. Vapalahti, , V. Paavilainen

This study describes KZR-8445, a cyclic depsipeptide that selectively inhibits Sec61-mediated protein biogenesis by mimicking signal peptides, showing efficacy in a rheumatoid arthritis model.

bioRxiv · Jul 3, 2022Biochemistry

New Vista into Origins of Viruses from a Prototypic ssDNA Phage

N. Kejzar, E. Laanto, I. Rissanen, V. Abrishami, M. Selvaraj, S. Moineau, J. Ravantti, L.-R. Sundberg, J. T. Huiskonen

The paper characterizes a lipid-containing ssDNA phage, {Phi}CjT23, and suggests it represents a model for the last common ancestor of ssDNA and dsDNA viruses in the Bamfordvirae.

bioRxiv · May 27, 2022 · PreprintMicrobiology

Tox_(R)CNN: Deep Learning-Based Nuclei Profiling tool For Drug Toxicity Screening

D. Jimenez-Carretero, V. Abrishami, L. Fernandez-de-Manuel, I. Palacios, A. Quilez-Alvarez, A. Diez-Sanchez, M. A. del Pozo, M. C. Montoya

This paper develops Tox-(R)CNN models to predict drug toxicity from nuclei images, showing improved sensitivity and broader specificity compared to existing methods.

bioRxiv · May 30, 2018Pharmacology & Toxicology