Predicting gene expression from DNA sequence remains challenging due to complex regulatory codes. We introduce a masked DNA language model pretrained on 165 fungal genomes closely related to budding y...

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Predicting expression profiles from RNA-seq experiments provides a powerful approach for universal sequence-based variant effect prediction, enabling researchers to score variants that affect total ge...

Genome-wide association studies (GWAS) have identified thousands of trait-associated loci. Prioritizing causal variants within these loci is critical for characterizing trait biology. Statistical fine...

DNA sequence deep learning models accurately predict epigenetic and transcriptional profiles, enabling analysis of gene regulation and genetic variant effects. While large-scale training models like E...

Join us at the Keystone Symposia on AI in Molecular Biology, September 2025, in Santa Fe, with field leaders!

Determining genetic variant effects on molecular phenotypes like gene expression is a task of paramount importance to medical genetics. DNA convolutional neural networks (CNNs) attain state-of-the-art...