11 citations · 12 across the 4 of their papers we have counts for
4 papers
Uncertainty-Guided Model Selection for Tabular Foundation Models in Biomolecule Efficacy Prediction
Jie Li, Andrew McCarthy, Zhizhuo Zhang +1
In-context learners like TabPFN are promising for biomolecule efficacy prediction, where established molecular feature sets and relevant experimental results can serve as powerful…
Fine-tuning Protein Language Models with Deep Mutational Scanning improves Variant Effect Prediction
Aleix Lafita, Ferran Gonzalez, Mahmoud Hossam +5
Protein Language Models (PLMs) have emerged as performant and scalable tools for predicting the functional impact and clinical significance of protein-coding variants, but they sti…
Generalising sequence models for epigenome predictions with tissue and assay embeddings
Jacob Deasy, Ron Schwessinger, Ferran Gonzalez +2
Sequence modelling approaches for epigenetic profile prediction have recently expanded in terms of sequence length, model size, and profile diversity. However, current models canno…
Epigenomic language models powered by Cerebras
Meredith V. Trotter, Cuong Q. Nguyen, Stephen Young +2
Large scale self-supervised pre-training of Transformer language models has advanced the field of Natural Language Processing and shown promise in cross-application to the biologic…