1 citations · 2 across the 5 of their papers we have counts for
6 papers
Interpreting Protein Language Model Embeddings via Orthogonal Projection for Protein Fitness Prediction
Paulo Yanez Sarmiento, Pia Francesca Rissom, Manuel Pfeuffer +6
Recently, there has been a growing adoption of protein language models (PLMs) in biomedical science. Their embeddings provide a rich numerical representation of protein sequences w…
Deterministic access to global viral sequence data enables robust agentic scientific discovery
Ferdous Nasri, Sarah Gurev, Patrick Varilly +6
Public viral genome resources such as the National Center for Biotechnology Information (NCBI) Virus database are central to outbreak response, evolutionary analysis, vaccine desig…
Evaluating Post-hoc Explanations of the Transformer-based Genome Language Model DNABERT-2
Isabel Kurth, Paulo Yanez Sarmiento, Bernhard Y. Renard
Explaining deep neural network predictions on genome sequences enables biological insight and hypothesis generation-often of greater interest than predictive performance alone. Whi…
BaGGLS: A Bayesian Shrinkage Framework for Interpretable Modeling of Interactions in High-Dimensional Biological Data
Marta S. Lemanczyk, Lucas Kock, Johanna Schlimme +2
Biological data sets are often high-dimensional, noisy, and governed by complex interactions among sparse signals. This poses major challenges for interpretability and reliable fea…
Sparse Explanations of Neural Networks Using Pruned Layer-Wise Relevance Propagation
Paulo Yanez Sarmiento, Simon Witzke, Nadja Klein +1
Explainability is a key component in many applications involving deep neural networks (DNNs). However, current explanation methods for DNNs commonly leave it to the human observer…
Identifying Drivers of Predictive Aleatoric Uncertainty
Pascal Iversen, Simon Witzke, Katharina Baum +1
Explainability and uncertainty quantification are key to trustable artificial intelligence. However, the reasoning behind uncertainty estimates is generally left unexplained. Ident…