Author

V. Gurev

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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Biophysics· 1 Pharmacology & Toxicology· 1 Physiology· 1

Signature Assays, Organisms & Methods

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Research Focus:drug response / drug discovery· 2 Approach:statistical model· 2 Research Focus:benchmarking· 1 Assay:clinical cohort / trial· 1 Organism:human· 1 Approach:machine learning (classical)· 1

Frequent Co-Authors

J. Kozloski· 2 shared J. Parikh· 2 shared O. Solovyova· 2 shared P. Di Achille· 2 shared S. Khamzin· 2 shared A. Bazhutina· 1 shared A. Dokuchaev· 1 shared D. Lebedev· 1 shared

Activity by Year

2019-2021 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 30 mutual co-author links

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2 shared papersBiophysics

J. Kozloski

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Author profile →

3 papers

Recent·Most cited

Machine Learning prediction of cardiac resynchronisation therapy response from combination of clinical and model-driven data

S. Khamzin, A. Dokuchaev, A. Bazhutina, T. Chumarnaya, S. Zubarev, T. Lyubimtseva, V. Lebedeva, D. Lebedev, V. Gurev, O. Solovyova

This study develops a machine learning model to predict cardiac resynchronization therapy (CRT) response by combining clinical data with model-driven biomarkers from personalized cardiac simulations.

Frontiers in Physiology · Dec 14, 2021Peer-reviewedPhysiology

Model order reduction for left ventricular mechanics via congruency training

P. Di Achille, J. Parikh, S. Khamzin, O. Solovyova, J. Kozloski, V. Gurev

This paper introduces a model order reduction strategy for 3-D finite element models of ventricular mechanics using congruency training and linear transformations to improve computational efficiency.

bioRxiv · Jul 10, 2019Biophysics

Intrinsic structure of model-derived metrics for in silico proarrhytmic risk assessment identified by global sensitivity analysis

J. Parikh, P. Di Achille, J. Kozloski, V. Gurev

This paper uses global sensitivity analysis to evaluate model-derived metrics for predicting drug-induced TdP risk, finding that these metrics are not strongly correlated with EAD generation and can be explained by direct features.

bioRxiv · Apr 16, 2019 · PreprintPharmacology & Toxicology