Article
Fitness translocation: improving variant effect prediction with biologically-grounded data augmentation.
Bioinformatics (Oxford, England) - 2 Jul 2026
Mialland Adrien, Fukunaga Shuzo, Katsuki Riku, Dong Yunfei, Yamaguchi Hideki, Saito Yutaka
Abstract excerpt
MOTIVATION: Data scarcity limits the characterization of protein fitness landscapes and the development of accurate variant effect prediction models. To address this challenge, we introduce fitness translocation, a data augmentation strategy that generates synthetic variants for a target protein by leveraging variant fitness data previously measured in homologous proteins. Using embeddings from protein language...
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