D-LIM Infers Low-Dimensional Gene–Gene Fitness Landscapes
Researchers (Wang et al.) publish on March 23, 2026 a neural network, D-LIM, that infers low-dimensional fitness landscapes directly from mutation–fitness data by modeling gene-specific molecular phenotypes whose nonlinear interactions determine fitness. Applied to deep mutational scanning of metabolic pathways, protein–protein interactions, and yeast environmental adaptation, D-LIM achieves state-of-the-art predictive accuracy and yields interpretable effective phenotypes; code and datasets are available on GitHub.
Scoring Rationale
Novel, peer-reviewed method with usable code and broad applicability; primarily focused on genotype-to-fitness domain, limiting general ML transfer.
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