ArticleProceedings of the National Academy of Sciences of the United States of America2022
On the sparsity of fitness functions and implications for learning.
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 24 papers.
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Who cites it
24 citing papers in PubMed.
- Graph-based representations in modern protein science.The Biochemical journal · 2026Review
- Gaugefixer: overcoming parameter non-identifiability in models of sequence-function relationships.BMC bioinformatics · 2026Article
- Direct estimation of genotype fitness from time series.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
- Investigating the determinants of performance in machine learning for protein fitness prediction.Protein science : a publication of the Protein Society · 2025Article
- Designing diverse and high-performance proteins with a large language model in the loop.PLoS computational biology · 2025Article
- Gauge fixing for sequence-function relationships.PLoS computational biology · 2025Article
- The simplicity of protein sequence-function relationships.Nature communications · 2024Article
- Gauge fixing for sequence-function relationships.bioRxiv : the preprint server for biology · 2024Article
- Article
- An extension of the Walsh-Hadamard transform to calculate and model epistasis in genetic landscapes of arbitrary shape and complexity.PLoS computational biology · 2024Article
- Opportunities and Challenges for Machine Learning-Assisted Enzyme Engineering.ACS central science · 2024Review
- The simplicity of protein sequence-function relationships.bioRxiv : the preprint server for biology · 2024Article
- Is Novelty Predictable?Cold Spring Harbor perspectives in biology · 2024Review
- Sparsity of higher-order landscape interactions enables learning and prediction for microbiomes.Proceedings of the National Academy of Sciences of the United States of America · 2023Article
- Machine Learning for Protein Engineering.ArXiv · 2023Article
- Research on schedling optimization of four-way shuttle-based storage and retrieval systems.Scientific reports · 2023Article
- Conformal prediction under feedback covariate shift for biomolecular design.Proceedings of the National Academy of Sciences of the United States of America · 2022Article
- Higher-order epistasis and phenotypic prediction.Proceedings of the National Academy of Sciences of the United States of America · 2022Article
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3 authors.
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Abstract
Fitness functions map biological sequences to a scalar property of interest. Accurate estimation of these functions yields biological insight and sets the foundation for model-based sequence design. However, the fitness datasets available to learn these functions are typically small relative to the large combinatorial space of sequences; characterizing how much data are needed for accurate estimation remains an open problem. There is a growing body of evidence demonstrating that empirical fitness functions display substantial sparsity when represented in terms of epistatic interactions. Moreover, the theory of Compressed Sensing provides scaling laws for the number of samples required to exactly recover a sparse function. Motivated by these results, we develop a framework to study the sparsity of fitness functions sampled from a generalization of the NK model, a widely used random field model of fitness functions. In particular, we present results that allow us to test the effect of the Generalized NK (GNK) model's interpretable parameters-sequence length, alphabet size, and assumed interactions between sequence positions-on the sparsity of fitness functions sampled from the model and, consequently, the number of measurements required to exactly recover these functions. We validate our framework by demonstrating that GNK models with parameters set according to structural considerations can be used to accurately approximate the number of samples required to recover two empirical protein fitness functions and an RNA fitness function. In addition, we show that these GNK models identify important higher-order epistatic interactions in the empirical fitness functions using only structural information.
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