Author

Kristi Xing

Columbia University

Listed by name: these papers carry no ORCID for this author, so different people with the same name may be mixed together.

Biology Disciplines

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Bioinformatics· 1 Neuroscience· 1

Signature Assays, Organisms & Methods

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Research Focus:cell type annotation· 1 Research Focus:cell-cell communication· 1 Approach:deep learning· 1 Organism:Drosophila· 1 Research Focus:drug response / drug discovery· 1 Research Focus:gene regulatory network inference· 1 Assay:scRNA-seq· 1 Approach:statistical model· 1

Frequent Co-Authors

Alexander Dunmyre· 1 shared Andrew D. EllingtoniD· 1 shared Anthony W. AzevedoiD· 1 shared Ayesha J. S. MahmoodiD· 1 shared Clayton W. KosonockyiD· 1 shared Daniel WinkleriD· 1 shared Despoina A. I. MavridouiD· 1 shared Edward M. MarcotteiD· 1 shared

Activity by Year

2025-2026 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 29 mutual co-author links

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1 shared paperBioinformatics

Alexander Dunmyre

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Author profile →

2 papers

Recent·Most cited

Learning from human and chemical languages to predict biological function

Clayton W. Kosonocky, Nikol Kadeřábková, Kangsan Kim, Ayesha J. S. Mahmood, Alexander Dunmyre, Phillip R. Woolley, Kristi Xing, Daniel Winkler, Tomer Babu, Filip Kadeřábek, Jonathan L. Sessler, Eric V. Anslyn, Edward M. Marcotte, Yan Zhang, Andrew D. Ellington, Despoina A. I. Mavridou

This paper introduces a deep learning model that predicts biological function from chemical structure using scientific literature, identifying effective inhibitors of beta-lactamases.

bioRxiv · Aug 17, 2026 · PreprintBioinformatics

Decoding neuronal wiring by joint inference of cell identity and synaptic connectivity

Himanshu Pawankumar Gupta, Anthony W. Azevedo, Yu‐Chieh Chen, Kristi Xing, Peter A. Sims, Erdem Varol, Richard S. Mann

The paper develops ConnectionMiner to integrate scRNAseq and connectome data, revealing gene expression patterns linked to synaptic connectivity in Drosophila leg motor circuits.

bioRxiv · Mar 4, 2025 · PreprintNeuroscience