Author

Kevin Dick

Carleton UniversityORCID 0000-0003-3931-523XUnclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Signature Assays, Organisms & Methods

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Assay:protein sequence· 3 Approach:statistical model· 2 Approach:deep learning· 1 Research Focus:drug response / drug discovery· 1 Organism:human· 1 Research Focus:protein design· 1 Research Focus:protein structure prediction· 1 Organism:virus· 1

Frequent Co-Authors

James R. GreeniD· 3 shared Alex WongiD· 1 shared Andrew LowiD· 1 shared Andrew Schoenrock· 1 shared Ashkan GolshaniiD· 1 shared Bahram SamanfariD· 1 shared Brad Barnes· 1 shared Clara Lettl· 1 shared

Activity by Year

2017-2023 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 45 mutual co-author links

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3 shared papersBioinformatics

James R. GreenORCID

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3 papers

Recent·Most cited

BioinformaticsbioRxivPreprintAug 4, 2023

Can a Sparse 29 x 29 Pixel Chaos Game Representation Predict Protein Binding Sites using Fine-Tuned State-of-the Art Deep Learning Semantic Segmentation Models?

Kevin Dick, James R. Green

This paper investigates using a fractal-based image representation and deep learning for predicting protein binding sites but concludes the approach is ineffective.

DOI 10.1101/2023.08.04.410498

protein sequencedeep learning

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BioinformaticsbioRxivPreprintMar 29, 2020

Computational Prediction of the Comprehensive SARS-CoV-2 vs. Human Interactome to Guide the Design of Therapeutics

Kevin Dick, Kyle K. Biggar, James R. Green

The paper predicts the SARS-CoV-2 vs. human interactome using sequence-based PPI predictors and identifies high-confidence interactions to guide therapeutic design.

7 citations

humanprotein sequence

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Synthetic BiologyComputational Biology and ChemistryPeer-reviewedNov 2, 2017

Designing Anti-Zika Virus Peptides Derived from Predicted Human-Zika Virus Protein-Protein Interactions

Thomas David Daniel Kazmirchuk, Kevin Dick, Daniel. J. Burnside…·Ashkan Golshani

This study predicts human-Zika virus protein-protein interactions and designs synthetic peptides to disrupt these interactions as potential anti-Zika virus therapeutics.

28 citationstop 5%

virusprotein sequence

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