Author

Rachael Kuintzle

California Institute of TechnologyORCID 0000-0002-1035-4983Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Bioinformatics· 1 Systems Biology· 1

Signature Assays, Organisms & Methods

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Assay:bulk RNA-seq· 1 Approach:deep learning· 1 Research Focus:differential expression· 1 Approach:experimental assay· 1 Research Focus:gene regulatory network inference· 1 Organism:human· 1 Assay:long-read sequencing· 1 Organism:mouse· 1

Frequent Co-Authors

A.T. Teegarden· 1 shared David A. HendrixiD· 1 shared Erich Merrill· 1 shared Leah A. SantatiD· 1 shared Michael B. ElowitziD· 1 shared Padideh Danaee· 1 shared Steven T. Hill· 1 shared

Activity by Year

2018-2025 · peak 1/yr

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7 top collaborators · 11 mutual co-author links

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1 shared paperBioinformatics

A.T. Teegarden

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2 papers

Recent·Most cited

Systems BiologyeLifePeer-reviewedJan 3, 2025

Diversity in Notch ligand-receptor signaling interactions

Rachael Kuintzle, Leah A. Santat, Michael B. Elowitz

The paper systematically characterizes Notch ligand-receptor signaling interactions, showing unique profiles of trans-activation, cis-inhibition, and cis-activation across different ligands and receptors.

9 citations

mouseexperimental assay

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BioinformaticsNucleic Acids ResearchPeer-reviewedJun 15, 2018

A Deep Recurrent Neural Network Discovers Complex Biological Rules to Decipher RNA Protein-Coding Potential

Steven T. Hill, Rachael Kuintzle, A.T. Teegarden…·David A. Hendrix

The paper trains a deep recurrent neural network to predict RNA protein-coding potential, finding it outperforms existing methods and reveals context-sensitive codons predictive of coding potential.

102 citationstop 4%

humanlong-read sequencing

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