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Although Mendel chose seven unrelated traits in peas to study gene segregation, most traits involve multiple gene interactions that create a spectrum of phenotypes. When the interaction of various genes or alleles at different locations influences a phenotype, this is called epistasis. Epistasis often involves one gene masking or interfering with the expression of another (antagonistic epistasis). Epistasis often occurs when different genes are part of the same biochemical pathway. The...
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Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
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D-LIM: A neural network for interpretable gene-gene interactions.

Shuhui Wang1,2, Alexandre Allauzen1,2, Philippe Nghe1,3

  • 1Laboratoire de Biophysique et Evolution, UMR CNRS-ESPCI 8231 Chimie Biologie Innovation, PSL University, Paris, France.

Plos Computational Biology
|March 23, 2026
PubMed
Summary
This summary is machine-generated.

Predicting gene mutation effects is hard. D-LIM, a new neural network, infers fitness landscapes from mutation data, offering interpretable results and accurate predictions for biological systems.

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Area of Science:

  • Genomics
  • Computational Biology
  • Systems Biology

Background:

  • Predicting the functional impact of genetic mutations, especially across multiple genes (epistasis), remains a significant challenge in biology.
  • Existing methods like biochemical models require extensive parameter knowledge, while standard machine learning approaches often lack biological interpretability.
  • Understanding genotype-fitness relationships is crucial for fields ranging from disease research to synthetic biology.

Purpose of the Study:

  • To develop a novel machine learning framework, D-LIM (Dimensionality-Limiting Inference of Molecular Phenotypes), for inferring interpretable low-dimensional fitness landscapes from mutation-fitness data.
  • To enable accurate prediction of mutation effects by modeling genes as independent molecular phenotypes with nonlinear interactions.
  • To provide a tool that not only predicts fitness but also reveals the underlying biological mechanisms and limitations of low-dimensional models.

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.