Peter Koo
Assistant Professor
Cancer Center Member
Ph.D., Yale University, 2015
koo@cshl.edu | 516-367-5520
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.
Can you outsmart this AI quiz?
February 6, 2023
Think you’re plugged into the latest artificial intelligence advancements? Test your tech knowledge with this quiz on AI and computational biology.
Finding the right AI for you
December 5, 2022
AI’s popularity has reached a point where there are too many options. How do you know which AI is right for you? CSHL scientists have a solution.
CSHL high schoolers finish top 10 in 2022 DREAM Challenge
October 7, 2022
The high school team competed against universities and private labs to build a computer program for predicting gene expression in yeast.
Regeneron competition honors CSHL high school researchers
March 22, 2022
Three high school student researchers at CSHL were among Regeneron Science Talent Search’s top 300 scholars. One made it to the final competition.
Making AI algorithms show their work
May 13, 2021
AI machines are often better than humans at discerning patterns. CSHL researchers developed a way to find out why.
AI researchers ask: What’s going on inside the black box?
February 8, 2021
Although researchers have figured out how to train computers to recognize things, they have yet to understand how machines make those predictions.
Peter Koo wants to understand how machines learn biology
September 20, 2019
Dr. Peter Koo joins the CSHL faculty as an assistant professor. His focus is on exploring how artificial intelligence integrates with biology and genomics.
All Publications
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
End-to-end learning of multiple sequence alignments with differentiable Smith-Waterman
01 Jan 2023 | Bioinformatics
Petti, Samantha, Bhattacharya, Nicholas, Rao, Roshan, Dauparas, Justas, Thomas, Neil, Zhou, Juannan, Rush, Alexander, Koo, Peter, Ovchinnikov, Sergey, Borgwardt, Karsten
ResidualBind: Uncovering Sequence-Structure Preferences of RNA-Binding Proteins with Deep Neural Networks
2023 | Methods in Molecular Biology | 2586:197-215
Koo, Peter, Ploenzke, Matt, Anand, Praveen, Paul, Steffan, Majdandzic, Antonio
Learning single-cell chromatin accessibility profiles using meta-analytic marker genes
22 Dec 2022 | Briefings in Bioinformatics | :bbac541
Karakida Kawaguchi, Risa, Tang, Ziqi, Fischer, Stephan, Rajesh, Chandana, Tripathy, Rohit, Koo, Peter, Gillis, Jesse
Evaluating deep learning for predicting epigenomic profiles
2022 | Nature Machine Intelligence
Toneyan, Shushan, Tang, Ziqi, Koo, Peter
Evolution-inspired augmentations improve deep learning for regulatory genomics
4 Nov 2022 | Cold Spring Harbor Laboratory
Lee, Nicholas, Tang, Ziqi, Toneyan, Shushan, Koo, Peter
Evaluating deep learning for predicting epigenomic profiles
1 May 2022 | bioRxiv
Toneyan, Shushan, Tang, Ziqi, Koo, Peter
Statistical correction of input gradients for black box models trained with categorical input features
1 May 2022 | bioRxiv
Majdandzic, Antonio, Koo, Peter
Interpreting Potts and Transformer Protein Models Through the Lens of Simplified Attention
2022 | Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing | 27:34-45
Bhattacharya, Nicholas, Thomas, Neil, Rao, Roshan, Dauparas, Justas, Koo, Peter, Baker, David, Song, Yun, Ovchinnikov, Sergey
End-to-end learning of multiple sequence alignments with differentiable Smith-Waterman
24 Oct 2021 | bioRxiv
Petti, Samantha, Bhattacharya, Nicholas, Rao, Roshan, Dauparas, Justas, Thomas, Neil, Zhou, Juannan, Rush, Alexander, Koo, Peter, Ovchinnikov, Sergey