Author

J. T. Balewski

Lawrence Berkeley National LaboratoryORCID 0000-0002-1899-6526Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Bioinformatics· 2 Neuroscience· 1

Signature Assays, Organisms & Methods

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Approach:deep learning· 3 Organism:bacteria (other)· 2 Research Focus:benchmarking· 1 Assay:electronic health records· 1 Research Focus:gene regulatory network inference· 1 Assay:protein sequence· 1 Approach:statistical model· 1 Research Focus:variant calling· 1

Frequent Co-Authors

Alexander Martin GelleriD· 1 shared Alicia ClumiD· 1 shared Anand SiththaranjaniD· 1 shared Asaf LevyiD· 1 shared Henry KyoungiD· 1 shared Kevin J. BenderiD· 1 shared Kristofer E. BouchardiD· 1 shared Kyung Geun Kim· 1 shared

Activity by Year

2019-2021 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 20 mutual co-author links

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1 shared paperBioinformatics

Alexander Martin GellerORCID

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3 papers

Recent·Most cited

Deeplasmid: Deep learning accurately separates plasmids from bacterial chromosomes

William Andreopoulos, Alexander Martin Geller, Miriam Lucke, J. T. Balewski, Alicia Clum, Natalia Ivanova, Asaf Levy

Deeplasmid uses deep learning to accurately distinguish plasmids from bacterial chromosomes in assembled genomes, achieving high precision and validating novel plasmid detection.

Nucleic Acids Research · Oct 25, 2021Peer-reviewedBioinformatics

Inferring neuronal ionic conductances from membrane potentials using CNNs

Roy Ben‐Shalom, J. T. Balewski, Anand Siththaranjan, Vyassa L. Baratham, Henry Kyoung, Kyung Geun Kim, Kevin J. Bender, Kristofer E. Bouchard

This paper uses CNNs to infer neuronal ionic conductances from simulated membrane potentials, achieving better accuracy than traditional optimization methods.

bioRxiv · Aug 6, 2019 · PreprintNeuroscience

Accurate prediction of bacterial two-component signaling with a deep recurrent neural network ORAKLE

J. T. Balewski, Zachary F. Hallberg

The paper trains a deep recurrent neural network to predict bacterial two-component signaling pairs, accurately recommending HK-RR pairs including orphans in Escherichia coli.

bioRxiv · Jan 28, 2019 · PreprintBioinformatics