ArticleNature communications2021
Epistatic Net allows the sparse spectral regularization of deep neural networks for inferring fitness functions.
Article in Nature communications, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers.
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Who cites it
28 citing papers in PubMed, 43 citations in OpenAlex.
- Graph-based representations in modern protein science.The Biochemical journal · 2026Review
- Direct estimation of genotype fitness from time series.bioRxiv : the preprint server for biology · 2026Article
- A minimal recurrent neural network models the robustness of interleaved practice on motor sequence learning.Scientific reports · 2026Article
- Structural ontogeny of protein-protein interactions.Science (New York, N.Y.) · 2026Article
- Distinguishing direct interactions from global epistasis using rank statistics.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Learning sequence-function relationships with scalable, interpretable Gaussian processes.bioRxiv : the preprint server for biology · 2025Article
- GOLF: A Generative AI Framework for Pathogenicity Prediction of Myocilin OLF Variants.bioRxiv : the preprint server for biology · 2025Article
- Importance of higher-order epistasis in protein sequence-function relationships.bioRxiv : the preprint server for biology · 2025Article
- Active learning-assisted directed evolution.Nature communications · 2025Article
- Protein stability models fail to capture epistatic interactions of double point mutations.Protein science : a publication of the Protein Society · 2025Article
- Considerations in the search for epistasis.Genome biology · 2024Review
- Protein stability models fail to capture epistatic interactions of double point mutations.bioRxiv : the preprint server for biology · 2024Article
- Addressing epistasis in the design of protein function.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- GWASTool: A web pipeline for detecting SNP-phenotype associations.Fundamental research · 2024Article
- InterpretingNature machine intelligence · 2024Article
- InterpretingbioRxiv : the preprint server for biology · 2024Article
- Opportunities and Challenges for Machine Learning-Assisted Enzyme Engineering.ACS central science · 2024Review
- Is Novelty Predictable?Cold Spring Harbor perspectives in biology · 2024Review
- Artificial intelligence-aided protein engineering: from topological data analysis to deep protein language models.Briefings in bioinformatics · 2023Review
- Article
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Authors and funding
8 authors at 2 institutions in 1 country.
Funding
Abstract
Despite recent advances in high-throughput combinatorial mutagenesis assays, the number of labeled sequences available to predict molecular functions has remained small for the vastness of the sequence space combined with the ruggedness of many fitness functions. While deep neural networks (DNNs) can capture high-order epistatic interactions among the mutational sites, they tend to overfit to the small number of labeled sequences available for training. Here, we developed Epistatic Net (EN), a method for spectral regularization of DNNs that exploits evidence that epistatic interactions in many fitness functions are sparse. We built a scalable extension of EN, usable for larger sequences, which enables spectral regularization using fast sparse recovery algorithms informed by coding theory. Results on several biological landscapes show that EN consistently improves the prediction accuracy of DNNs and enables them to outperform competing models which assume other priors. EN estimates the higher-order epistatic interactions of DNNs trained on massive sequence spaces-a computational problem that otherwise takes years to solve.
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