ArticleProceedings of the National Academy of Sciences of the United States of America2022
Interpretable modeling of genotype-phenotype landscapes with state-of-the-art predictive power.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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Who cites it
23 citing papers in PubMed.
- A simple model captures key characteristics of biological non-deterministic genotype-phenotype maps.PLoS computational biology · 2026Article
- Generative design of synthetic gene circuits for functional and evolutionary properties.NPJ systems biology and applications · 2026Article
- D-LIM: A neural network for interpretable gene-gene interactions.PLoS computational biology · 2026Article
- Inference and Visualization of Complex Genotype-Phenotype Maps.Molecular biology and evolution · 2026Article
- Accurate variant effect estimation in FACS-based deep mutational scanning data with Lilace.Genome biology · 2026Article
- Characterization of the heterogeneity in SARS-CoV-2 fitness dynamics via graph representation learning.PLoS computational biology · 2026Article
- Variant scoring tools for deep mutational scanning.Molecular systems biology · 2025Review
- Distinguishing direct interactions from global epistasis using rank statistics.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Inference and visualization of complex genotype-phenotype maps withbioRxiv : the preprint server for biology · 2025Article
- Learning sequence-function relationships with scalable, interpretable Gaussian processes.bioRxiv : the preprint server for biology · 2025Article
- Importance of higher-order epistasis in protein sequence-function relationships.bioRxiv : the preprint server for biology · 2025Article
- Rore: robust and efficient antioxidant protein classification via a novel dimensionality reduction strategy based on learning of fewer features.Genomics & informatics · 2024Article
- MoCHI: neural networks to fit interpretable models and quantify energies, energetic couplings, epistasis, and allostery from deep mutational scanning data.Genome biology · 2024Article
- Designing interpretable deep learning applications for functional genomics: a quantitative analysis.Briefings in bioinformatics · 2024Review
- A parameterized two-domain thermodynamic model explains diverse mutational effects on protein allostery.eLife · 2024Article
- InterpretingNature machine intelligence · 2024Article
- InterpretingbioRxiv : the preprint server for biology · 2024Article
- A parametrized two-domain thermodynamic model explains diverse mutational effects on protein allostery.bioRxiv : the preprint server for biology · 2024Article
- Advances in ligand-specific biosensing for structurally similar molecules.Cell systems · 2023Review
- Environmental modulation of global epistasis in a drug resistance fitness landscape.Nature communications · 2023Article
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3 authors.
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Abstract
Large-scale measurements linking genetic background to biological function have driven a need for models that can incorporate these data for reliable predictions and insight into the underlying biophysical system. Recent modeling efforts, however, prioritize predictive accuracy at the expense of model interpretability. Here, we present LANTERN (landscape interpretable nonparametric model, https://github.com/usnistgov/lantern), a hierarchical Bayesian model that distills genotype-phenotype landscape (GPL) measurements into a low-dimensional feature space that represents the fundamental biological mechanisms of the system while also enabling straightforward, explainable predictions. Across a benchmark of large-scale datasets, LANTERN equals or outperforms all alternative approaches, including deep neural networks. LANTERN furthermore extracts useful insights of the landscape, including its inherent dimensionality, a latent space of additive mutational effects, and metrics of landscape structure. LANTERN facilitates straightforward discovery of fundamental mechanisms in GPLs, while also reliably extrapolating to unexplored regions of genotypic space.
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