Author

Catherine Kim

Princeton UniversityORCID 0000-0001-9237-0532Unclaimed ORCID profile · follow to get notified of new preprints

Additional preprints under this name without ORCID

Catherine Kim1 paper · Haverford College

Biology Disciplines

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Bioinformatics· 1 Neuroscience· 1

Signature Assays, Organisms & Methods

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Research Focus:cell-cell communication· 1 Approach:deep learning· 1 Research Focus:differential expression· 1 Research Focus:drug response / drug discovery· 1 Assay:electronic health records· 1 Research Focus:gene regulatory network inference· 1 Organism:human· 1 Approach:machine learning (classical)· 1

Frequent Co-Authors

Adeen FlinkeriD· 1 shared Aditi Rao· 1 shared Aditi SinghiD· 1 shared Ariel GoldsteiniD· 1 shared Avinatan HassidimiD· 1 shared Bobbi Aubrey· 1 shared Daniel J. FriedmaniD· 1 shared Gina ChoeiD· 1 shared

Activity by Year

2021-2023 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 45 mutual co-author links

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1 shared paperNeuroscience

Adeen FlinkerORCID

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2 papers

Recent·Most cited

Deep speech-to-text models capture the neural basis of spontaneous speech in everyday conversations

Ariel Goldstein, Haocheng Wang, Leonard Niekerken, Zaid Zada, Bobbi Aubrey, Tom Sheffer, Samuel A. Nastase, Harshvardhan Gazula, Mariano Schain, Aditi Singh, Aditi Rao, Gina Choe, Catherine Kim, Werner Doyle, Daniel J. Friedman, , Uri Hasson

The paper uses a deep speech-to-text model to predict neural responses during spontaneous conversations, revealing a distributed cortical hierarchy for speech and language processing.

bioRxiv · Jun 26, 2023 · PreprintNeuroscience

Prediction of adverse drug reactions associated with drug-drug interactions using hierarchical classification

Catherine Kim, Nicholas P. Tatonetti

This study presents a hierarchical machine learning model to predict drug-drug interaction-associated adverse drug reactions and provides pharmacological insights, achieving ~91% accuracy.

bioRxiv · Feb 10, 2021 · PreprintBioinformatics