Accès ouvert
2026
dataset
OpenAlex
Michael T. Pearce, Thomas Dooms, Ryō Yamamoto, Joshua Meehl et autres
This dataset contains the precomputed variant effect predictions and interpretability features that power the Evo Variant Effect Explorer (EVEE) web application, accompanying the preprint "EVEE: Interpretable variant effect prediction from genomic foundation model embeddings" (Pearce et al., 2026, doi:10.64898/2026.04.10.717844). Each row is …
de, us
(code pays fourni par la source)
Accès ouvert
2026
dataset
OpenAlex
Michael T. Pearce, Thomas Dooms, Ryō Yamamoto, Joshua Meehl et autres
This dataset contains the precomputed variant effect predictions and interpretability features that power the Evo Variant Effect Explorer (EVEE) web application, accompanying the preprint "EVEE: Interpretable variant effect prediction from genomic foundation model embeddings" (Pearce et al., 2026, doi:10.64898/2026.04.10.717844). Each row is …
de, us
(code pays fourni par la source)
Accès ouvert
2026
preprint
OpenAlex
Michael T Pearce, Thomas Dooms, Ryō Yamamoto, Shant Ayanian et autres
Abstract Scientific foundation models learn high-dimensional representations from diverse data modalities, yet what they encode and how to extract that knowledge remain open questions. Here we show that probing the internal representations of Evo 2, a 7-billion-parameter genomic foundation model, enables accurate …
us, fi
(code pays fourni par la source)
Accès ouvert
2025
preprint
OpenAlex
Joshua Meehl, Prasad Siddavatam
Abstract This study investigates the application of advanced predictive models to reduce the cost and effort associated with protein engineering campaigns. We explore the use of protein language models (PLMs), a variant of large language models (LLMs), to predict functional performance from …
us
(code pays fourni par la source)