Karollus, Alexander

AlphaGenome: advancing regulatory variant effect prediction with a unified DNA sequence model

Avsec, Ž., Latysheva, N., Cheng, J., Novati, G., Taylor, K. R., Ward, T., Bycroft, C., Nicolaisen, L., Arvaniti, E., Pan, J., et al. (2025) · Nature

Genomics and Chromatin DynamicsGenetic Associations and Epidemiology

Cited by 395

Advancing regulatory variant effect prediction with AlphaGenome

Avsec, Ž., Latysheva, N., Cheng, J., Novati, G., Taylor, K. R., Ward, T., Bycroft, C., Nicolaisen, L., Arvaniti, E., Pan, J., et al. (2026) · Nature

Genomics and Chromatin DynamicsGenetic Associations and Epidemiology

Cited by 373

Current sequence-based models capture gene expression determinants in promoters but mostly ignore distal enhancers

Karollus, A., Mauermeier, T., & Gagneur, J. (2023) · Genome Biology

Genomics and Chromatin DynamicsGene Regulatory Network Analysis

Cited by 128

Predicting mean ribosome load for 5’UTR of any length using deep learning

Karollus, A., Avsec, Ž., & Gagneur, J. (2021) · PLOS Computational Biology

RNA and protein synthesis mechanismsRNA Research and Splicing

Cited by 57

Predicting Mean Ribosome Load for 5’UTR of any length using Deep Learning

Karollus, A., Avsec, Ž., & Gagneur, J. (2020) · PLoS Computational Biology

RNA and protein synthesis mechanismsRNA Research and Splicing

Cited by 57