Author

Nicholas Keone Lee

Cold Spring Harbor LaboratoryORCID 0000-0002-2756-1102Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Signature Assays, Organisms & Methods

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Approach:deep learning· 3 Research Focus:gene regulatory network inference· 3 Research Focus:benchmarking· 2 Assay:DNA methylation· 2 Approach:statistical model· 2 Research Focus:differential expression· 1 Organism:mouse· 1 Assay:scATAC-seq· 1

Frequent Co-Authors

Peter K. KooiD· 2 shared Benjamin J. StewartiD· 1 shared Jacob HepkemaiD· 1 shared Martin HembergiD· 1 shared Menna Ruth ClatworthyiD· 1 shared Rohan Ghotra· 1 shared Rohit K. TripathyiD· 1 shared Shushan ToneyaniD· 1 shared

Activity by Year

2021-2023 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 19 mutual co-author links

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2 shared papersBioinformatics

Peter K. KooORCID

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3 papers

Recent·Most cited

Predicting the impact of sequence motifs on gene regulation using single-cell data

Jacob Hepkema, Nicholas Keone Lee, Benjamin J. Stewart, Siwat Ruangroengkulrith, Varodom Charoensawan, Menna Ruth Clatworthy, Martin Hemberg

The paper uses a convolutional neural network to identify regulatory motifs and their cell type-specific importance from single-cell data, explaining 29% of gene expression variance in mouse tissues.

Genome biology · Aug 15, 2023Peer-reviewedBioinformatics

EvoAug: improving generalization and interpretability of genomic deep neural networks with evolution-inspired data augmentations

Nicholas Keone Lee, Ziqi Tang, Shushan Toneyan, Peter K. Koo

EvoAug improves genomic DNN generalization and interpretability through evolution-inspired data augmentations while preserving functional integrity.

Genome biology · May 4, 2023Peer-reviewedBioinformatics

Designing Interpretable Convolution-Based Hybrid Networks for Genomics

Rohan Ghotra, Nicholas Keone Lee, Rohit K. Tripathy, Peter K. Koo

This study investigates how architectural choices in convolution-based hybrid networks affect motif representations and the interpretability of their attention maps in genomics.

bioRxiv · Jul 13, 2021 · PreprintBioinformatics