ArticleNature methods2018
Deep generative models of genetic variation capture the effects of mutations.
Article in Nature methods, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 309 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
309 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Variant effect predictors: a systematic review and practical guide.Human genetics · 2024Pooled it
- DESIGN: a framework for generative AI-enabled crop molecular design-driven breeding.Science China. Life sciences · 2026Review
- Democratizing protein language model training, sharing and collaboration.Nature biotechnology · 2026Article
- Hierarchical Organization of Evolutionary Constraint in Duplicated EF-Hand Motifs.Journal of molecular evolution · 2026Article
- Protein function-conditioned language models for variant effect prediction and controllable design.Biophysical journal · 2026Article
- Functional alignment of protein language models via reinforcement learning.Nature communications · 2026Article
- Leveraging latent space models for enzyme discovery and sampling.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Enhancing prediction accuracy for enzyme activity engineering through sequence co-evolution and epistatic relationship modeling.Nucleic acids research · 2026Article
- The fitness landscape of a form II rubisco in a photosynthetic bacterium guides engineering of oxygen tolerance.Science advances · 2026Article
- The Evolution of Artificial Intelligence in Antibody Design: From Structure-Based Engineering to Generative Models.Antibodies (Basel, Switzerland) · 2026Review
- Structure-coevolution dual-guided engineering of C-glycosyltransferase enables high-level biosynthesis of vitexin inSynthetic and systems biotechnology · 2026Article
- Unveiling the terra cognita of sequence spaces using Cartesian projection of asymmetric distances.NAR genomics and bioinformatics · 2026Article
- Evolutionary profiles for protein fitness prediction.Bioinformatics (Oxford, England) · 2026Article
- Deep Contrastive Learning for High-Throughput Prediction of Drug Resistance Mutations from Sequences.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Decoding cryptic defluorinases through a latent generative sequence landscape.Chemical science · 2026Article
- Artificial intelligence and automation in enzyme engineering: evolution, advances, and future perspectives.Bioresources and bioprocessing · 2026Review
- Generative AI for controllable protein sequence design: A survey.npj drug discovery · 2026Review
- Decoding variants of uncertain significance in systemic autoinflammatory diseases.Nature reviews. Rheumatology · 2026Review
- Machine-learning prediction of affinity and epistasis in the bovine pancreatic trypsin inhibitor-chymotrypsin complex.Protein science : a publication of the Protein Society · 2026Article
- Deep learning-guided engineering of SpuFz1 and rational miniaturization of ωRNA enables efficient genome editing.Nature communications · 2026Article
249 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
The functions of proteins and RNAs are defined by the collective interactions of many residues, and yet most statistical models of biological sequences consider sites nearly independently. Recent approaches have demonstrated benefits of including interactions to capture pairwise covariation, but leave higher-order dependencies out of reach. Here we show how it is possible to capture higher-order, context-dependent constraints in biological sequences via latent variable models with nonlinear dependencies. We found that DeepSequence ( https://github.com/debbiemarkslab/DeepSequence ), a probabilistic model for sequence families, predicted the effects of mutations across a variety of deep mutational scanning experiments substantially better than existing methods based on the same evolutionary data. The model, learned in an unsupervised manner solely on the basis of sequence information, is grounded with biologically motivated priors, reveals the latent organization of sequence families, and can be used to explore new parts of sequence space.
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Registered trials
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.