ArticleNature communications2020
Minimum epistasis interpolation for sequence-function relationships.
Article in Nature communications, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers.
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Who cites it
29 citing papers in PubMed, 46 citations in OpenAlex.
- Inference of fitness landscapes with heterogeneous patterns of epistasis across sites.bioRxiv : the preprint server for biology · 2026Article
- How far can you go? Extrapolating values of catalytic activity from known protein landscapes in natural and directed evolution.Chemical Society reviews · 2026Review
- On learning functions over biological sequence space: relating Gaussian process priors, regularization, and gauge fixing.Journal of mathematical biology · 2026Article
- Bacterial proteome foundation model enhances functional prediction from enzymes to ecological interactions.bioRxiv : the preprint server for biology · 2026Article
- Inference and Visualization of Complex Genotype-Phenotype Maps.Molecular biology and evolution · 2026Article
- Distinguishing direct interactions from global epistasis using rank statistics.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Risk prediction models for biochemical recurrence of Chinese prostate cancer patients after radical prostatectomy based on magnetic resonance imaging examination: a systematic review.Quantitative imaging in medicine and surgery · 2025Review
- 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
- Investigating the determinants of performance in machine learning for protein fitness prediction.Protein science : a publication of the Protein Society · 2025Article
- Scaling the fitness effects of mutations with respect to differentially adapted Arabidopsis thaliana accessions under natural conditions.Evolution; international journal of organic evolution · 2025Article
- Importance of higher-order epistasis in protein sequence-function relationships.bioRxiv : the preprint server for biology · 2025Article
- Gauge fixing for sequence-function relationships.PLoS computational biology · 2025Article
- The genetic architecture of protein stability.Nature · 2024Article
- Strangers in a foreign land: 'Yeastizing' plant enzymes.Microbial biotechnology · 2024Review
- Gauge fixing for sequence-function relationships.bioRxiv : the preprint server for biology · 2024Article
- Robust genetic codes enhance protein evolvability.PLoS biology · 2024Article
- An extension of the Walsh-Hadamard transform to calculate and model epistasis in genetic landscapes of arbitrary shape and complexity.PLoS computational biology · 2024Article
- Pervasive epistasis exposes intramolecular networks in adaptive enzyme evolution.Nature communications · 2023Article
- Master regulators of biological systems in higher dimensions.Proceedings of the National Academy of Sciences of the United States of America · 2023Article
Corrections and comments
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
Abstract
Massively parallel phenotyping assays have provided unprecedented insight into how multiple mutations combine to determine biological function. While such assays can measure phenotypes for thousands to millions of genotypes in a single experiment, in practice these measurements are not exhaustive, so that there is a need for techniques to impute values for genotypes whose phenotypes have not been directly assayed. Here, we present an imputation method based on inferring the least epistatic possible sequence-function relationship compatible with the data. In particular, we infer the reconstruction where mutational effects change as little as possible across adjacent genetic backgrounds. The resulting models can capture complex higher-order genetic interactions near the data, but approach additivity where data is sparse or absent. We apply the method to high-throughput transcription factor binding assays and use it to explore a fitness landscape for protein G.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.