Peter K. Koo

15 papers Journal 13Unranked 1
YearRankTypeTitle / Venue / Authors
2026 J jnl
Towards interpretable prediction of recurrence risk in breast cancer using pathology foundation models.
npj Digit. Medicine
Jakub R. Kaczmarzyk, Sarah C. Van Alsten, Alyssa J. Cozzo, Rajarsi Gupta, Peter K. Koo, Melissa A. Troester, Katherine A. Hoadley, Joel H. Saltz
2025 — ed.
Proceedings of the 20th Machine Learning in Computational Biology (MLCB), 10-11 September 2025, New York, NY, USA
MLCB
David A. Knowles, Peter K. Koo
2025 J jnl
Reusable specimen-level inference in computational pathology.
CoRR
Jakub R. Kaczmarzyk, Rishul Sharma, Peter K. Koo, Joel H. Saltz
2024 J jnl
EvoAug-TF: extending evolution-inspired data augmentations for genomic deep learning to TensorFlow.
Bioinform.
Yiyang Yu, Shivani Muthukumar, Peter K. Koo
2024 J jnl
Interpreting cis-regulatory mechanisms from genomic deep neural networks using surrogate models.
Nat. Mac. Intell.
Evan Seitz, David M. McCandlish, Justin B. Kinney, Peter K. Koo
2023 J jnl
ChampKit: A framework for rapid evaluation of deep neural networks for patch-based histopathology classification.
Comput. Methods Programs Biomed.
Jakub R. Kaczmarzyk, Rajarsi Gupta, Tahsin M. Kurç, Shahira Abousamra, Joel H. Saltz, Peter K. Koo
2023 J jnl
End-to-end learning of multiple sequence alignments with differentiable Smith-Waterman.
Bioinform.
Samantha Petti, Nicholas Bhattacharya, Roshan Rao, Justas Dauparas, Neil Thomas, Juannan Zhou, Alexander M. Rush, Peter K. Koo, Sergey Ovchinnikov
2023 J jnl
Learning single-cell chromatin accessibility profiles using meta-analytic marker genes.
Briefings Bioinform.
Risa Karakida Kawaguchi, Ziqi Tang, Stephan Fischer, Chandana Rajesh, Rohit Tripathy, Peter K. Koo, Jesse A. Gillis
2022 J jnl
Evaluating deep learning for predicting epigenomic profiles.
Nat. Mac. Intell.
Shushan Toneyan, Ziqi Tang, Peter K. Koo
2022 J jnl
Evaluating histopathology transfer learning with ChampKit.
CoRR
Jakub R. Kaczmarzyk, Tahsin M. Kurç, Shahira Abousamra, Rajarsi Gupta, Joel H. Saltz, Peter K. Koo
2022 — conf
Selecting deep neural networks that yield consistent attribution-based interpretations for genomics.
MLCB
Antonio Majdandzic, Chandana Rajesh, Ziqi Tang, Shushan Toneyan, Ethan L. Labelson, Rohit K. Tripathy, Peter K. Koo
2021 J jnl
Global importance analysis: An interpretability method to quantify importance of genomic features in deep neural networks.
PLoS Comput. Biol.
Peter K. Koo, Antonio Majdandzic, Matthew Ploenzke, Praveen Anand, Steffan B. Paul
2021 J jnl
Improving representations of genomic sequence motifs in convolutional networks with exponential activations.
Nat. Mach. Intell.
Peter K. Koo, Matt Ploenzke
2019 J jnl
Representation learning of genomic sequence motifs with convolutional neural networks.
PLoS Comput. Biol.
Peter K. Koo, Sean R. Eddy
2015 J jnl
Extracting Diffusive States of Rho GTPase in Live Cells: Towards
PLoS Comput. Biol.
Peter K. Koo, Matthew Weitzman, Chandran R. Sabanaygam, Kenneth L. van Golen, Simon G. J. Mochrie