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Peter K. Koo
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2020 – today
- 2024
- [j10]Yiyang Yu
, Shivani Muthukumar, Peter K. Koo
:
EvoAug-TF: extending evolution-inspired data augmentations for genomic deep learning to TensorFlow. Bioinform. 40(3) (2024) - [j9]Evan Seitz
, David M. McCandlish
, Justin B. Kinney
, Peter K. Koo
:
Interpreting cis-regulatory mechanisms from genomic deep neural networks using surrogate models. Nat. Mac. Intell. 6(6): 701-713 (2024) - 2023
- [j8]Risa Karakida Kawaguchi
, Ziqi Tang
, Stephan Fischer
, Chandana Rajesh, Rohit Tripathy, Peter K. Koo
, Jesse A. Gillis
:
Learning single-cell chromatin accessibility profiles using meta-analytic marker genes. Briefings Bioinform. 24(1) (2023) - [j7]Samantha Petti, Nicholas Bhattacharya, Roshan Rao, Justas Dauparas, Neil Thomas, Juannan Zhou, Alexander M. Rush, Peter K. Koo
, Sergey Ovchinnikov:
End-to-end learning of multiple sequence alignments with differentiable Smith-Waterman. Bioinform. 39(1) (2023) - [j6]Jakub R. Kaczmarzyk
, Rajarsi Gupta
, Tahsin M. Kurç, Shahira Abousamra
, Joel H. Saltz
, Peter K. Koo
:
ChampKit: A framework for rapid evaluation of deep neural networks for patch-based histopathology classification. Comput. Methods Programs Biomed. 239: 107631 (2023) - 2022
- [j5]Shushan Toneyan, Ziqi Tang
, Peter K. Koo
:
Evaluating deep learning for predicting epigenomic profiles. Nat. Mac. Intell. 4(12): 1088-1100 (2022) - [c1]Antonio Majdandzic, Chandana Rajesh, Ziqi Tang, Shushan Toneyan, Ethan L. Labelson, Rohit K. Tripathy, Peter K. Koo:
Selecting deep neural networks that yield consistent attribution-based interpretations for genomics. MLCB 2022: 131-149 - [i1]Jakub R. Kaczmarzyk, Tahsin M. Kurç, Shahira Abousamra, Rajarsi Gupta, Joel H. Saltz, Peter K. Koo:
Evaluating histopathology transfer learning with ChampKit. CoRR abs/2206.06862 (2022) - 2021
- [j4]Peter K. Koo
, Matt Ploenzke:
Improving representations of genomic sequence motifs in convolutional networks with exponential activations. Nat. Mach. Intell. 3(3): 258-266 (2021) - [j3]Peter K. Koo
, Antonio Majdandzic, Matthew Ploenzke, Praveen Anand, Steffan B. Paul
:
Global importance analysis: An interpretability method to quantify importance of genomic features in deep neural networks. PLoS Comput. Biol. 17(5) (2021)
2010 – 2019
- 2019
- [j2]Peter K. Koo
, Sean R. Eddy
:
Representation learning of genomic sequence motifs with convolutional neural networks. PLoS Comput. Biol. 15(12) (2019) - 2015
- [j1]Peter K. Koo, Matthew Weitzman, Chandran R. Sabanaygam, Kenneth L. van Golen, Simon G. J. Mochrie:
Extracting Diffusive States of Rho GTPase in Live Cells: Towards In Vivo Biochemistry. PLoS Comput. Biol. 11(10) (2015)
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