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Peter Koo

Peter Koo

Associate 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

Uncertainty-aware genomic deep learning with knowledge distillation

15 Nov 2024 | bioRxiv
Zhou, Jessica;  Rizzo, Kaeli;  Tang, Ziqi;  Koo, Peter;  

A community effort to optimize sequence-based deep learning models of gene regulation

11 Oct 2024 | Nature Biotechnology
Rafi, Abdul;  Nogina, Daria;  Penzar, Dmitry;  Lee, Dohoon;  Lee, Danyeong;  Kim, Nayeon;  Kim, Sangyeup;  Kim, Dohyeon;  Shin, Yeojin;  Kwak, Il-Youp;  Meshcheryakov, Georgy;  Lando, Andrey;  Zinkevich, Arsenii;  Kim, Byeong-Chan;  Lee, Juhyun;  Kang, Taein;  Vaishnav, Eeshit;  Yadollahpour, Payman;  Random Promoter DREAM Challenge Consortium;  Kim, Sun;  Albrecht, Jake;  Regev, Aviv;  Gong, Wuming;  Kulakovskiy, Ivan;  Meyer, Pablo;  de Boer, Carl;  

Analysis of single-cell CRISPR perturbations indicates that enhancers predominantly act multiplicatively

10 Oct 2024 | Cell Genomics | :100672
Zhou, Jessica;  Guruvayurappan, Karthik;  Toneyan, Shushan;  Chen, Hsiuyi;  Chen, Aaron;  Koo, Peter;  McVicker, Graham;  

Interpreting cis-regulatory interactions from large-scale deep neural networks

16 Sep 2024 | Nature Genetics
Toneyan, Shushan;  Koo, Peter;  

Explainable AI for computational pathology identifies model limitations and tissue biomarkers

4 Sep 2024
Kaczmarzyk, Jakub;  Koo, Peter;  Saltz, Joel;  

Massive experimental quantification of amyloid nucleation allows interpretable deep learning of protein aggregation.

1 Oct 2024 | bioRxiv
Thompson, Mike;  Martín, Mariano;  Olmo, Trinidad;  Rajesh, Chandana;  Koo, Peter;  Bolognesi, Benedetta;  Lehner, Ben;  

Analysis of single-cell CRISPR perturbations indicates that enhancers act multiplicatively and provides limited evidence for epistatic-like interactions

9 Jul 2024 | bioRxiv
Zhou, Jessica;  Guruvayurappan, Karthik;  Toneyan, Shushan;  Chen, Hsiuyi;  Chen, Aaron;  Koo, Peter;  McVicker, Graham;  

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

Jun 2024 | Nature Machine Intelligence | 6(6):701-713
Seitz, E;  McCandlish, D;  Kinney, J;  Koo, P;  

Designing DNA With Tunable Regulatory Activity Using Discrete Diffusion

24 May 2024 | bioRxiv
Sarkar, Anirban;  Tang, Ziqi;  Zhao, Chris;  Koo, Peter;  

Evaluating the representational power of pre-trained DNA language models for regulatory genomics

4 Mar 2024 | bioRxiv
Tang, Ziqi;  Koo, Peter;  

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