D-LIM: A neural network for interpretable gene-gene interactions.

Main Methods:

• Introduction of D-LIM, a neural network architecture designed to infer fitness landscapes directly from mutation-fitness data.
• D-LIM assumes that genes contribute through independent, gene-specific molecular phenotypes whose interactions determine overall fitness.
• Application of D-LIM to diverse biological datasets, including deep mutational scanning data from metabolic pathways, protein-protein interactions, and yeast adaptation studies.

Main Results:

• D-LIM achieved state-of-the-art predictive accuracy across various biological systems, outperforming existing methods.
• The model successfully inferred interpretable effective phenotypes, revealing the nature of gene interactions and identifying potential trade-offs.
• In cases where the low-dimensional assumption failed, D-LIM effectively indicated the insufficiency of such models, guiding further investigation.
• The model demonstrated the ability to estimate mutational effects on inferred phenotypes, allowing for limited extrapolation beyond the training data.

Conclusions:

• D-LIM offers a powerful and interpretable approach to modeling genotype-fitness relationships, particularly in complex biological systems.
• The framework provides insights into the structure of epistasis and the validity of low-dimensional approximations in biological fitness landscapes.
• By integrating structure constraints within a neural network, D-LIM facilitates biological inference and hypothesis generation, advancing our understanding of gene function and evolution.