Loading publication...

Noodle always counts identifier-free feature events for service operation. With your permission, Google Analytics can also estimate audience and engagement. Search text, publication identifiers, cookies, IP addresses and browser fingerprints are never included in Noodle's first-party event counters.

Privacy details

Article

Efficient epistasis inference via higher-order covariance matrix factorization.

Genetics - 6 Aug 2025

Shimagaki Kai S, Barton John P

Abstract excerpt

Epistasis can profoundly influence evolutionary dynamics. Temporal genetic data, consisting of sequences sampled repeatedly from a population over time, provides a unique resource to understand how epistasis shapes evolution. However, detecting epistatic interactions from sequence data is technically challenging. Existing methods for identifying epistasis are computationally demanding, limiting their...

Read the complete abstract on PubMed

Topics

Share this publication in a Topic to start or enrich a Post.

Share to Algorithms
Share to Computer Simulation
Share to Epistasis, Genetic

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Share to Evolution, Molecular

Share to Models, Genetic

Share to Mutation

Source

Open in PubMed Open full text

Identifiers

PMID: 40540490
DOI: 10.1093/genetics/iyaf118
PMCID: PMC12341948

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 2 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

Efficient epistasis inference via higher-order covariance matrix factorization.PMID 40540490Read ↗

Inferring Epistasis from Genetic Time-series Data.Semantic proximity Read ↗

The importance of epistasis in the evolution of viral pathogensSemantic proximity Read ↗

Widespread Historical Contingency in Influenza VirusesSemantic proximity Read ↗

Widespread Historical Contingency in Influenza Viruses.Semantic proximity Read ↗

Global analysis of more than 50,000 SARS-CoV-2 genomes reveals epistasis between eight viral genesSemantic proximity Read ↗

An information-gain approach to detecting three-way epistatic interactions in genetic association studies.Semantic proximity Read ↗

Evaluation of Existing Methods for High-Order Epistasis Detection.Semantic proximity Read ↗

Higher order interactions: detection of epistasis using machine learning and evolutionary computation.Semantic proximity Read ↗

Real-time identification of epistatic interactions in SARS-CoV-2 from large genome collections.Semantic proximity Read ↗

CINOEDV: a co-information based method for detecting and visualizing n-order epistatic interactions.Semantic proximity Read ↗

Strong epistatic selection on the RNA secondary structure of HIV.Semantic proximity Read ↗

Epi-SSA: A novel epistasis detection method based on a multi-objective sparrow search algorithm.Semantic proximity Read ↗

⌁Select a neighboring publication to make it the new centre.