Author

Rajeev Jaundoo

University of AlbertaORCID 0000-0001-7223-4322Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Approach:machine learning (classical)· 2 Research Focus:benchmarking· 1 Research Focus:drug response / drug discovery· 1

Frequent Co-Authors

Jack A. TuszyńskiiD· 2 shared Travis J. A. CraddockiD· 2 shared

Activity by Year

2024-2024 · peak 2/yr

Co-Author Collaboration Network

2 top collaborators · 1 mutual co-author link

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2 shared papersBioinformatics

Jack A. TuszyńskiORCID

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2 papers

Recent·Most cited

BioinformaticsbioRxivPreprintMay 3, 2024

Application of Supervised Machine Learning Models for Drug-Action Prediction Towards Nuclear Type I Receptors

Rajeev Jaundoo, Jack A. Tuszyński, Travis J. A. Craddock

This study compares machine learning models for predicting drug action on nuclear type I receptors, finding decision tree and naive Bayes models to be most efficient and accurate.

1 citation

machine learning (classical)drug response / drug discovery

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BioinformaticsbioRxivPreprintApr 30, 2024

Supervised Machine Learning for Bioelectrical Cellular Networks

Rajeev Jaundoo, Travis J. A. Craddock, Jack A. Tuszyński

The paper trains machine learning models to replace core functions of BETSE for more efficient bioelectrical network simulations.

DOI 10.1101/2024.04.30.591880

machine learning (classical)benchmarking

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