Author

Viatcheslav Gurev

IBM Research - Thomas J. Watson Research CenterORCID 0000-0003-3304-6251Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

Click to filter author papers

Neuroscience· 1 Pharmacology & Toxicology· 1

Signature Assays, Organisms & Methods

Click to filter · or open tag

Research Focus:benchmarking· 1 Approach:deep learning· 1 Research Focus:differential expression· 1 Research Focus:drug response / drug discovery· 1 Assay:electronic health records· 1 Organism:mouse· 1 Approach:statistical model· 1

Frequent Co-Authors

James KozloskiiD· 2 shared Casey O. DiekmaniD· 1 shared Francesco TamagniniiD· 1 shared Jaimit Parikh· 1 shared Kyle C. A. WedgwoodiD· 1 shared Paolo Di AchilleiD· 1 shared Soheil SaghafiiD· 1 shared Timothy H. RumbelliD· 1 shared

Activity by Year

2019-2024 · peak 1/yr

Co-Author Collaboration Network

8 top collaborators · 18 mutual co-author links

Click any collaborator node to inspect or filter shared papers

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

Curved chords = mutual co-authorship

2 shared papersNeuroscience

James KozloskiORCID

Mutual co-authors in cluster

Author profile →

2 papers

Recent·Most cited

NeuroscienceBulletin of Mathematical BiologyPeer-reviewedMar 25, 2024

Inferring parameters of pyramidal neuronexcitability in mouse models of Alzheimer'sdisease using biophysical modeling and deeplearning

Soheil Saghafi, Timothy H. Rumbell, Viatcheslav Gurev…·Casey O. Diekman

This paper introduces DeepHM, a method combining deep learning and biophysical modeling to infer ion channel conductance changes in Alzheimer's mouse models.

3 citations

mouseelectronic health records

𝕏 PostBluesky

Pharmacology & ToxicologybioRxivPreprintApr 16, 2019

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

Jaimit Parikh, Paolo Di Achille, James Kozloski, Viatcheslav 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.

DOI 10.1101/543926

statistical modeldrug response / drug discovery

𝕏 PostBluesky