The Brandes Lab at New York University uses AI to understand our genomes and fight disease.
We are developing and using genomic AI, including protein and DNA language models, to identify disease-causing mutations, prediagnose and treat disease, and better understand our genomes.
Genomic heterogeneity inflates the performance of variant pathogenicity predictions. bioRxiv. (2025)
P-KNN: Maximizing variant classification evidence through joint calibration of multiple pathogenicity prediction tools. bioRxiv. (2025)
Genome-wide prediction of disease variant effects with a deep protein language model. Nature Genetics. (2023)
ProteinBERT: a universal deep-learning model of protein sequence and function. Bioinformatics. (2022)
PWAS: proteome-wide association study—linking genes and phenotypes by functional variation in proteins. Genome Biology. (2020)
The language of proteins: NLP, machine learning & protein sequences. Computational and Structural Biotechnology. (2021)
Open problems in human trait genetics. Genome Biology. (2022)
Mitigation of chromosome loss in clinical CRISPR-Cas9-engineered T cells. Cell. (2023)
Genetic association studies of alterations in protein function expose recessive effects on cancer predisposition. Nature Scientific Reports. (2021)
Quantifying gene selection in cancer through protein functional alteration bias. Nucleic Acids Research. (2019)
We are hiring at all levels (students, postdocs, and staff).
The lab is affiliated with both the School of Medicine and the Courant Institute of Mathematical Sciences, and accepts students across different programs.
