K
Kevin Dick
Researcher at Carleton University
Publications - 30
Citations - 215
Kevin Dick is an academic researcher from Carleton University. The author has contributed to research in topics: Computer science & Medicine. The author has an hindex of 6, co-authored 22 publications receiving 99 citations.
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Journal ArticleDOI
Deep Learning for Critical Infrastructure Resilience
TL;DR: The resiliency of critical infrastructures is essential in modern society, but much of the deployed infrastructure has yet to fully leverage modern technical developments.
Journal ArticleDOI
Reciprocal Perspective for Improved Protein-Protein Interaction Prediction.
Kevin Dick,James R. Green +1 more
TL;DR: Data visualization techniques are used to show that no single decision threshold is suitable for all protein pairs, given the inherent diversity of protein interaction profiles, and a novel modeling framework called Reciprocal Perspective (RP) is introduced, which estimates a localized threshold on a per-protein basis using several rank order metrics.
Journal ArticleDOI
PIPE4: Fast PPI Predictor for Comprehensive Inter- and Cross-Species Interactomes
Kevin Dick,Bahram Samanfar,Bahram Samanfar,Bradley Barnes,Elroy R. Cober,Benjamin Mimee,Le Hoa Tan,Stephen J. Molnar,Kyle K. Biggar,Ashkan Golshani,Frank Dehne,James R. Green +11 more
TL;DR: Comparing PIPE4 with the state-of-the-art resulted in improved performance, indicative that it should be the method of choice for complex PPI prediction schemas.
Journal ArticleDOI
Designing anti-Zika virus peptides derived from predicted human-Zika virus protein-protein interactions.
Tom Kazmirchuk,Kevin Dick,Daniel Burnside,Brad Barnes,Houman Moteshareie,Maryam Hajikarimlou,Katayoun Omidi,Duale Ahmed,Andrew Low,Clara Lettl,Mohsen Hooshyar,Andrew Schoenrock,Sylvain Pitre,Mohan Babu,Edana Cassol,Bahram Samanfar,Alex Wong,Frank Dehne,James R. Green,Ashkan Golshani +19 more
TL;DR: The design of several synthetic competitive inhibitory peptides against key pathogenic ZIKV proteins through the prediction of protein-protein interactions (PPIs) is reported on, which constitute a foundational resource to aid in the multi-disciplinary effort to combat ZikV infection.
Journal ArticleDOI
RPmirDIP: Reciprocal Perspective improves miRNA targeting prediction
TL;DR: It is demonstrated that miRNA target prediction can be significantly improved through the application of the Reciprocal Perspective (RP) method, a cascaded, semi-supervised machine learning method originally developed for protein-protein interaction prediction.