Peter K. Koo

According to our database, Peter K. Koo authored at least 12 papers between 2015 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

EvoAug-TF: extending evolution-inspired data augmentations for genomic deep learning to TensorFlow.

Yiyang Yu, Shivani Muthukumar, Peter K. Koo

Bioinform., March, 2024

Interpreting cis-regulatory mechanisms from genomic deep neural networks using surrogate models.

Evan Seitz, David M. McCandlish, Justin B. Kinney, Peter K. Koo

Nat. Mac. Intell., 2024

2023

ChampKit: A framework for rapid evaluation of deep neural networks for patch-based histopathology classification.

Jakub R. Kaczmarzyk, Rajarsi Gupta, Tahsin M. Kurç, Shahira Abousamra, Joel H. Saltz, Peter K. Koo

Comput. Methods Programs Biomed., September, 2023

End-to-end learning of multiple sequence alignments with differentiable Smith-Waterman.

Samantha Petti, Nicholas Bhattacharya, Roshan Rao, Justas Dauparas, Neil Thomas, Juannan Zhou, Alexander M. Rush, Peter K. Koo, Sergey Ovchinnikov

Bioinform., January, 2023

Learning single-cell chromatin accessibility profiles using meta-analytic marker genes.

Risa Karakida Kawaguchi, Ziqi Tang, Stephan Fischer, Chandana Rajesh, Rohit Tripathy, Peter K. Koo, Jesse A. Gillis

Briefings Bioinform., January, 2023

2022

Evaluating deep learning for predicting epigenomic profiles.

Shushan Toneyan, Ziqi Tang, Peter K. Koo

Nat. Mac. Intell., December, 2022

Evaluating histopathology transfer learning with ChampKit.

Jakub R. Kaczmarzyk, Tahsin M. Kurç, Shahira Abousamra, Rajarsi Gupta, Joel H. Saltz, Peter K. Koo

CoRR, 2022

Selecting deep neural networks that yield consistent attribution-based interpretations for genomics.

Antonio Majdandzic, Chandana Rajesh, Ziqi Tang, Shushan Toneyan, Ethan L. Labelson, Rohit K. Tripathy, Peter K. Koo

Proceedings of the Machine Learning in Computational Biology, 21-22 November 2022, Online, 2022

2021

Global importance analysis: An interpretability method to quantify importance of genomic features in deep neural networks.

Peter K. Koo, Antonio Majdandzic, Matthew Ploenzke, Praveen Anand, Steffan B. Paul

PLoS Comput. Biol., 2021

Improving representations of genomic sequence motifs in convolutional networks with exponential activations.

Peter K. Koo, Matt Ploenzke

Nat. Mach. Intell., 2021

2019

Representation learning of genomic sequence motifs with convolutional neural networks.

Peter K. Koo, Sean R. Eddy

PLoS Comput. Biol., 2019

2015

Extracting Diffusive States of Rho GTPase in Live Cells: Towards <i>In Vivo</i> Biochemistry.

Peter K. Koo, Matthew Weitzman, Chandran R. Sabanaygam, Kenneth L. van Golen, Simon G. J. Mochrie

PLoS Comput. Biol., 2015