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

NeurosciencebioRxivPreprintJun 26, 2023

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

Ariel Goldstein, Haocheng Wang, Leonard Niekerken…·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.

18 citations

humanelectronic health records

𝕏 PostBluesky

BioinformaticsbioRxivPreprintFeb 10, 2021

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.

2 citations

machine learning (classical)drug response / drug discovery

𝕏 PostBluesky