ArticleGenome biology2022
MAVE-NN: learning genotype-phenotype maps from multiplex assays of variant effect.
Article in Genome biology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 60 papers.
What it found
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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.
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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.
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Who cites it
60 citing papers in PubMed.
- Interpretable biophysical neural networks of transcriptional activation domains separate roles of protein abundance and coactivator binding.Cell systems · 2026Article
- Gaugefixer: overcoming parameter non-identifiability in models of sequence-function relationships.BMC bioinformatics · 2026Article
- Toward generalizable and interpretable AI in regulatory genomics.Nature genetics · 2026Review
- Combining machine learning and iterative experiments to keep pace with emerging viral variants of concern.PLoS computational biology · 2026Article
- Machine Learning-Driven Simulations of the SARS-CoV-2 Fitness Landscape from Deep Mutational Scanning Experiments.Journal of chemical information and modeling · 2026Article
- GaugeFixer: overcoming parameter non-identifiability in models of sequence-function relationships.bioRxiv : the preprint server for biology · 2026Article
- Mount Fuji's stubby peak: the genotypic density of additive landscapes near maximal fitness.bioRxiv : the preprint server for biology · 2026Article
- On learning functions over biological sequence space: relating Gaussian process priors, regularization, and gauge fixing.Journal of mathematical biology · 2026Article
- PoolParty: streamlined design of DNA sequence libraries in Python.bioRxiv : the preprint server for biology · 2026Article
- Genetic background shapes AI-predicted variant effects.bioRxiv : the preprint server for biology · 2026Article
- Predictive modeling of gene expression and localization of DNA binding site using deep convolutional neural networks.PLoS computational biology · 2026Article
- Supervised learning of protein variant effects across large-scale mutagenesis datasets.Protein science : a publication of the Protein Society · 2026Article
- D-LIM: A neural network for interpretable gene-gene interactions.PLoS computational biology · 2026Article
- Inference and Visualization of Complex Genotype-Phenotype Maps.Molecular biology and evolution · 2026Article
- Accurate variant effect estimation in FACS-based deep mutational scanning data with Lilace.Genome biology · 2026Article
- Artificial intelligence directed computational protein design: lessons from COVID-19 for pandemic-ready vaccines and antibody therapeutics.Journal of pharmacy & pharmaceutical sciences : a publication of the Canadian Society for Pharmaceutical Sciences, Societe canadienne des sciences pharmaceutiques · 2026Review
- Using PyBioNetFit to leverage qualitative and quantitative data in biological model parameterization and uncertainty quantification.Frontiers in immunology · 2026Article
- Liquid Biopsy and Multi-Omic Biomarkers in Breast Cancer: Innovations in Early Detection, Therapy Guidance, and Disease Monitoring.Biomedicines · 2025Review
- Quantified Dynamics-Property Relationships: Data-Efficient Protein Engineering with Machine Learning of Protein Dynamics.Journal of chemical information and modeling · 2025Article
- Control of gene output by intron RNA structure.bioRxiv : the preprint server for biology · 2025Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
Multiplex assays of variant effect (MAVEs) are a family of methods that includes deep mutational scanning experiments on proteins and massively parallel reporter assays on gene regulatory sequences. Despite their increasing popularity, a general strategy for inferring quantitative models of genotype-phenotype maps from MAVE data is lacking. Here we introduce MAVE-NN, a neural-network-based Python package that implements a broadly applicable information-theoretic framework for learning genotype-phenotype maps-including biophysically interpretable models-from MAVE datasets. We demonstrate MAVE-NN in multiple biological contexts, and highlight the ability of our approach to deconvolve mutational effects from otherwise confounding experimental nonlinearities and noise.
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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.