6 articles tagged with #variant effect prediction.
An evidence-first review of genomic foundation models across clinical molecular diagnostic workflows. Compares DNABERT-2, Nucleotide Transformer, HyenaDNA, Caduceus, Evo 2, and AlphaGenome against established clinical tools across eight diagnostic tasks and post-calling variant effect prediction.
Two H100s on different clouds gave bit-identical Evo 2 scores for 3,893 BRCA1 variants. An L40S running the same container reshuffled two thirds of the ranking. Running the model in FP32 brought the two GPUs back into agreement, at three to six times the compute.
If you can test 100 variants in a splicing reporter, which score should rank them? On 8,297 held-out MFASS variants, a simple trained baseline put 61 splice-disrupting variants in its first 100, and SpliceAI and Pangolin put 63 to 66. The difference was not established. Pangolin ranked the whole list better in exploratory analyses.
One pretrained genomic model can look dominant on a leaderboard and lose to a linear baseline on the next task. Two evidence ledgers explain why.
Carbon-3B matches Evo2-7B on sequence recovery, variant-effect prediction, and motif-perturbation discrimination while generating DNA over 150 times faster.
AlphaGenome processes 1 million DNA base pairs to predict variant effects across 7,000+ genomic tracks in one second, outperforming specialized models on 25 of 26 VEP benchmarks.
