Peter Koo

Peter Koo

Assistant Professor
Cancer Center Member

Ph.D., Yale University, 2015

koo@cshl.edu | 516-367-5520

Koo Lab Website   Faculty Profile

Deep learning has the potential to make a significant impact in basic biology and cancer, but a major challenge is understanding the reasons behind their predictions. My research develops methods to interpret this powerful class of black box models, with a goal of elucidating data-driven insights into the underlying mechanisms of sequence-function relationships.

Deep learning is being applied rapidly in many areas of genomics, demonstrating improved performance over previous methods on benchmark datasets. Despite the promise of deep learning, it remains unclear whether improved predictions will translate to new biological discoveries because of their low interpretability, which has earned them a reputation as a black box. Understanding the reasons underlying a deep learning model’s prediction may reveal new biological insights not captured by previous methods. Our group develops methods to interpret high-performing deep learning models to distill knowledge that they learn from big, noisy, biological sequence data. Our goal is to elucidate biological mechanisms that underlie sequence-function relationships for gene regulation and protein (dys)function. Recently, we have teamed up with other members of the CSHL Cancer Center to investigate the sequence basis of epigenomic differences across healthy and cancer cells.

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All Publications

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

18 Jan 2024 | bioRxiv
Yu, Yiyang, Muthukumar, Shivani, Koo, Peter

Current approaches to genomic deep learning struggle to fully capture human genetic variation

Dec 2023 | Nature Genetics | 55(12):2021-2022
Tang, Ziqi, Toneyan, Shushan, Koo, Peter

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

16 Nov 2023 | bioRxiv
Seitz, Evan, McCandlish, David, Kinney, Justin, Koo, Peter

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

Sep 2023 | Computer Methods and Programs in Biomedicine | 239:107631
Kaczmarzyk, Jakub, Gupta, Rajarsi, Kurc, Tahsin, Abousamra, Shahira, Saltz, Joel, Koo, Peter

Interpreting Cis -Regulatory Interactions from Large-Scale Deep Neural Networks for Genomics

3 Jul 2023 | bioRxiv
Toneyan, Shushan, Koo, Peter

Correcting gradient-based interpretations of deep neural networks for genomics

9 May 2023 | Genome Biology | 24(1):109
Majdandzic, Antonio, Rajesh, Chandana, Koo, Peter

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

5 May 2023 | Genome Biology | 24(1):105
Lee, Nicholas, Tang, Ziqi, Toneyan, Shushan, Koo, Peter

ETV6 dependency in Ewing sarcoma by antagonism of EWS-FLI1-mediated enhancer activation

19 Jan 2023 | Nature Cell Biology
Gao, Yuan, He, Xue-Yan, Wu, Xiaoli, Huang, Yu-Han, Toneyan, Shushan, Ha, Taehoon, Ipsaro, Jonathan, Koo, Peter, Joshua-Tor, Leemor, Bailey, Kelly, Egeblad, Mikala, Vakoc, Christopher

Light and temperature regulate m6A-RNA modification to regulate growth in plants

17 Jan 2023 | bioRxiv
Artz, Oliver, Ackermann, Amanda, Taylor, Laura, Koo, Peter, Pedmale, Ullas

ResidualBind: Uncovering Sequence-Structure Preferences of RNA-Binding Proteins with Deep Neural Networks

2023 | Methods in molecular biology (Clifton, N.J.) | 2586:197-215
Koo, Peter, Ploenzke, Matt, Anand, Praveen, Paul, Steffan, Majdandzic, Antonio

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