ReviewPLoS computational biology2015
Inferring Pairwise Interactions from Biological Data Using Maximum-Entropy Probability Models.
Review in PLoS computational biology, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 60 papers.
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Who cites it
60 citing papers in PubMed.
- On learning functions over biological sequence space: relating Gaussian process priors, regularization, and gauge fixing.Journal of mathematical biology · 2026Article
- Unveiling gene perturbation effects through gene regulatory networks inference from single-cell transcriptomic data.PLoS computational biology · 2026Article
- Inference and Visualization of Complex Genotype-Phenotype Maps.Molecular biology and evolution · 2026Article
- Forecasting drug resistant HIV protease evolution.PLoS computational biology · 2026Article
- Predicting side chain conformations in folded proteins by AlphaFold: Perspective and challenges.Biophysical journal · 2025Article
- Inference and visualization of complex genotype-phenotype maps withbioRxiv : the preprint server for biology · 2025Article
- Introduction to correlation networks: Interdisciplinary approaches beyond thresholding.Physics reports · 2025Article
- Discovering the nuclear localization signal universe through a deep learning model with interpretable attention units.Patterns (New York, N.Y.) · 2025Article
- Using AlphaFold2 to Predict the Conformations of Side Chains in Folded Proteins.bioRxiv : the preprint server for biology · 2025Article
- Gauge fixing for sequence-function relationships.PLoS computational biology · 2025Article
- A hierarchical Bayesian inference model for volatile multivariate exponentially distributed signals.Frontiers in computational neuroscience · 2025Article
- Energy landscape analysis based on the Ising model: Tutorial review.PLOS complex systems · 2025Article
- Gauge fixing for sequence-function relationships.bioRxiv : the preprint server for biology · 2024Article
- The quality and complexity of pairwise maximum entropy models for large cortical populations.PLoS computational biology · 2024Article
- Computational Approaches to Predict Protein-Protein Interactions in Crowded Cellular Environments.Chemical reviews · 2024Review
- Engineering Proteins Using Statistical Models of Coevolutionary Sequence Information.Cold Spring Harbor perspectives in biology · 2024Review
- Ising-like model replicating time-averaged spiking behaviour of in vitro neuronal networks.Scientific reports · 2024Article
- Self-supervised deep learning of gene-gene interactions for improved gene expression recovery.Briefings in bioinformatics · 2024Article
- Large language models generate functional protein sequences across diverse families.Nature biotechnology · 2023Article
- Scale-free correlations and potential criticality in weakly ordered populations of brain cancer cells.Science advances · 2023Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Maximum entropy-based inference methods have been successfully used to infer direct interactions from biological datasets such as gene expression data or sequence ensembles. Here, we review undirected pairwise maximum-entropy probability models in two categories of data types, those with continuous and categorical random variables. As a concrete example, we present recently developed inference methods from the field of protein contact prediction and show that a basic set of assumptions leads to similar solution strategies for inferring the model parameters in both variable types. These parameters reflect interactive couplings between observables, which can be used to predict global properties of the biological system. Such methods are applicable to the important problems of protein 3-D structure prediction and association of gene-gene networks, and they enable potential applications to the analysis of gene alteration patterns and to protein design.
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