Peter K. Koo

Publication Activity (10 Years)

Years Active: 2015-2024
Publications (10 Years): 12

Top Topics

Manifold Alignment

Advances In Data Mining

Top Venues

PLoS Comput. Biol.

Nat. Mac. Intell.

Publications

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) (2024)
Yiyang Yu, Shivani Muthukumar, Peter K. Koo
EvoAug-TF: extending evolution-inspired data augmentations for genomic deep learning to TensorFlow. Bioinform. 40 (3) (2024)
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)
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 (2023)
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)
Jakub R. Kaczmarzyk, Tahsin M. Kurç, Shahira Abousamra, Rajarsi Gupta, Joel H. Saltz, Peter K. Koo
Evaluating histopathology transfer learning with ChampKit. CoRR (2022)
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)
Shushan Toneyan, Ziqi Tang, Peter K. Koo
Evaluating deep learning for predicting epigenomic profiles. Nat. Mac. Intell. 4 (12) (2022)
Peter K. Koo, Matt Ploenzke
Improving representations of genomic sequence motifs in convolutional networks with exponential activations. Nat. Mach. Intell. 3 (3) (2021)
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)
Peter K. Koo, Sean R. Eddy
Representation learning of genomic sequence motifs with convolutional neural networks. PLoS Comput. Biol. 15 (12) (2019)
Peter K. Koo, Matthew Weitzman, Chandran R. Sabanaygam, Kenneth L. van Golen, Simon G. J. Mochrie
Biochemistry. PLoS Comput. Biol. 11 (10) (2015)