Scientific

Evan E Seitz, David M McCandlish, Justin B Kinney and Peter K Koo. “Decoding the mechanistic impact of genetic variation on regulatory sequences with deep learning”, Biology of Genomes, May 2025.

Evan E Seitz, David M McCandlish, Justin B Kinney and Peter K Koo. “Decoding the Mechanistic Impact of Genetic Variation on Regulatory Sequences with Deep Learning”, ICLR 2025 Workshop on Generative and Experimental Perspectives for Biomolecular Design, Singapore, April 2025.

Evan E Seitz, David M McCandlish, Justin B Kinney and Peter K Koo. “Deciphering the determinants of mechanistic variation in regulatory sequences”, Probabilistic Modeling in Genomics, March 2025.

Evan E Seitz, David M McCandlish, Justin B Kinney and Peter K Koo. “Deciphering the determinants of mechanistic variation in regulatory sequences”, Biological Data Science, November 2024.

Evan E Seitz, David M McCandlish, Justin B Kinney and Peter K Koo. “A surrogate modeling framework for interpreting deep neural networks in functional genomics”, Genome Informatics, December 2023.

SQUID (Surrogate Quantitative Interpretability for Deepnets) is a Python suite to interpret sequence-based deep learning models for regulatory genomics data with domain-specific surrogate models. This software was developed in the Kinney lab and Koo lab at Cold Spring Harbor Laboratory.

Evan E Seitz, Justin B Kinney and Peter K Koo. “A surrogate modeling framework for interpreting deep neural networks in functional genomics”, Biology of Genomes, May 2023.

Public release of ManifoldEM Python suite (Beta version) for determination of conformational continua of biomolecules from single-particle cryo-EM data. This software was developed in the Frank research group at Columbia University in collaboration with members from UWM.