The Koo Lab studies gene regulation through a computational lens using data-driven machine learning solutions. Our approach develops deep learning methods to infer sequence-function relationships that underlie high-throughput functional genomics data. Through model interpretability, we aim to elucidate cis-regulatory mechanisms -- the complex coordination of sequence elements such as motifs -- with a broader aim of advancing precision medicine for complex diseases, including cancer. We are part of the Simons Center for Quantitative Biology and the NCI-designated Cancer Center at Cold Spring Harbor Laboratory.



Lab News

  • May 24, 2024 -- Anirban's new work on "Designing DNA With Tunable Regulatory Activity Using Discrete Diffusion" is on bioRxiv!
  • May 20, 2024 -- Moon Nagai, SBS grad student, joins Koo Lab! Welcome Moon!
  • May 10, 2024 -- Alessandro gives oral presentation at Biology of Genomes on how active learning can be used to design optimal training data for genomic deep learning!
  • May 2, 2024 -- Evan is awarded the NIH F32 (Ruth L. Kirschstein Postdoctoral Individual National Research Service Award)!
  • Apr 24, 2024 -- Amber passes her thesis defense! Congrats Dr. Ziqi Tang!
  • Apr 17, 2024 -- Shush passes her thesis defense! Congrats Dr. Shushan Toneyan!
  • Apr 8, 2024 -- SQUID manuscript is accepted in Nature Machine Intelligence! Congrats Evan!
  • Apr 4, 2024 -- Pretty is awarded the NSF Graduate Research Fellowship! Congrats!
  • Mar 8, 2024 -- EvoAug-TF is published in Bioinformatics here.
  • Mar 4, 2024 -- Amber's work on "Evaluating the representational power of pre-trained DNA language models for regulatory genomics" in on bioRxiv!