ArticleProceedings of the National Academy of Sciences of the United States of America2022
Conformal prediction under feedback covariate shift for biomolecular design.
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 15 papers.
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Who cites it
15 citing papers in PubMed.
- From rules to foundation models: a comprehensive review of machine learning approaches for siRNA design.NAR genomics and bioinformatics · 2026Review
- Predictive performance precision analysis in medicine: identification of low-confidence predictions at patient and profile levels (MED3pa I).Journal of the American Medical Informatics Association : JAMIA · 2026Article
- Explicit Applicability Domain Calculations Can Help Determine When Uncertainty Estimates Are Less Reliable.ACS omega · 2026Article
- Active learning-assisted directed evolution.Nature communications · 2025Article
- Functional protein mining with conformal guarantees.Nature communications · 2025Article
- Reliable machine learning models in genomic medicine using conformal prediction.Frontiers in bioinformatics · 2025Review
- Benchmarking uncertainty quantification for protein engineering.PLoS computational biology · 2025Article
- ProteinReDiff: Complex-based ligand-binding proteins redesign by equivariant diffusion-based generative models.Structural dynamics (Melville, N.Y.) · 2024Article
- mbtransfer: Microbiome intervention analysis using transfer functions and mirror statistics.PLoS computational biology · 2024Article
- A systematic analysis of regression models for protein engineering.PLoS computational biology · 2024Article
- Online Calibrated and Conformal Prediction Improves Bayesian Optimization.Proceedings of machine learning research · 2024Article
- Opportunities and Challenges for Machine Learning-Assisted Enzyme Engineering.ACS central science · 2024Review
- Is Novelty Predictable?Cold Spring Harbor perspectives in biology · 2024Review
- Safe and reliable transport of prediction models to new healthcare settings without the need to collect new labeled data.medRxiv : the preprint server for health sciences · 2023Article
- Machine Learning for Protein Engineering.ArXiv · 2023Article
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Authors and funding
5 authors.
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Abstract
Many applications of machine-learning methods involve an iterative protocol in which data are collected, a model is trained, and then outputs of that model are used to choose what data to consider next. For example, a data-driven approach for designing proteins is to train a regression model to predict the fitness of protein sequences and then use it to propose new sequences believed to exhibit greater fitness than observed in the training data. Since validating designed sequences in the wet laboratory is typically costly, it is important to quantify the uncertainty in the model's predictions. This is challenging because of a characteristic type of distribution shift between the training and test data that arises in the design setting-one in which the training and test data are statistically dependent, as the latter is chosen based on the former. Consequently, the model's error on the test data-that is, the designed sequences-has an unknown and possibly complex relationship with its error on the training data. We introduce a method to construct confidence sets for predictions in such settings, which account for the dependence between the training and test data. The confidence sets we construct have finite-sample guarantees that hold for any regression model, even when it is used to choose the test-time input distribution. As a motivating use case, we use real datasets to demonstrate how our method quantifies uncertainty for the predicted fitness of designed proteins and can therefore be used to select design algorithms that achieve acceptable tradeoffs between high predicted fitness and low predictive uncertainty.
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