Author

Christopher Y. Park

Flatiron Institute

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Biology Disciplines

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Bioinformatics· 2 Genomics· 1

Signature Assays, Organisms & Methods

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Approach:deep learning· 3 Research Focus:gene regulatory network inference· 3 Research Focus:variant effect prediction· 3 Assay:whole-genome sequencing· 3 Research Focus:differential expression· 2 Assay:DNA methylation· 2 Organism:human· 2 Research Focus:benchmarking· 1

Frequent Co-Authors

Chandra L. TheesfeldiD· 3 shared Olga G. TroyanskayaiD· 3 shared Aaron K. WongiD· 1 shared Alan I. PackeriD· 1 shared Aviya LitmaniD· 1 shared Claudia ScheckeliD· 1 shared Jian ZhouiD· 1 shared John J. FakiD· 1 shared

Activity by Year

2019-2026 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 33 mutual co-author links

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3 shared papersBioinformatics

Chandra L. TheesfeldORCID

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3 papers

Recent·Most cited

Variant-resolved prediction of context-specific isoform variation with a graph-based attention model

Aviya Litman, Zhicheng Pan, Ksenia Sokolova, Joyce Fang, Tess A. Marvin, Natalie Sauerwald, Christopher Y. Park, Chandra L. Theesfeld, Olga G. Troyanskaya

Otari predicts tissue-specific isoform abundance changes caused by genetic variants using a graph-based attention model trained on genomic and long-read transcriptome data.

Cell Genomics · Jan 16, 2026Peer-reviewedBioinformatics

An automated framework for efficiently designing deep convolutional neural networks in genomics

Zijun Zhang, Christopher Y. Park, Chandra L. Theesfeld, Olga G. Troyanskaya

This paper introduces AMBER, an automated framework for designing deep CNNs in genomics, showing it outperforms non-NAS and expert-designed models in accuracy.

Nature Machine Intelligence · Mar 15, 2021Peer-reviewedBioinformatics

Whole-genome deep learning analysis reveals causal role of noncoding mutations in autism

Jian Zhou, Christopher Y. Park, Chandra L. Theesfeld, Aaron K. Wong, Yuan Yuan, Claudia Scheckel, John J. Fak, Julien Funk, Kevin Yao, Yoko Tajima, Alan I. Packer, Robert B. Darnell, Olga G. Troyanskaya

The paper uses deep learning to analyze whole-genome data from ASD families, showing noncoding mutations significantly impact transcriptional and post-transcriptional regulation and contribute to autism.

Nature Genetics · May 27, 2019Peer-reviewedGenomics