Article

LagCI Enables Inference of Temporal Causal Relationships from Dense Multi-Omic Time Series

2026-04-18

Abstract excerpt

Inferring causal relationships from time-series data is critical for uncovering the dynamics of biological regulation. However, in multi-omics studies, this task is often hampered by sparse temporal sampling and the limitations of existing methods. To address this, we developed Lagged-Correlation Based Causal Inference (lagCI), a computational framework designed to identify time-lagged associations by combining co...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work: 19bd1c7a-dd4f-5830-a9de-d67be1e3bb4d
DOI: 10.64898/2026.04.15.718654

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

LagCI Enables Inference of Temporal Causal Relationships from Dense Multi-Omic Time SeriesDOI 10.64898/2026.04.15.718654Read

TIMEOR: a web-based tool to uncover temporal regulatory mechanisms from multi-omics dataSemantic proximity Read

Temporal Biodynamics: An AI Platform for Identification of Stage-Relevant Targets and BiomarkersSemantic proximity Read

Causally-anchored multi-omic deep learning recovers exercise-responsive and ageing-causal genes from human physical activitySemantic proximity Read

Phenome-Wide Association Study of Actigraphy in the UK BiobankSemantic proximity Read

Temporal response characterization across individual multiomics profiles of prediabetic and diabetic subjectsSemantic proximity Read

Monitoring the circadian clock in human blood using personalized machine learningSemantic proximity Read

Robust Identification of Temporal Biomarkers in Longitudinal Omics StudiesSemantic proximity Read

Essential Regression - a generalizable framework for inferring causal latent factors from multi-omic human datasetsSemantic proximity Read

11 million days of longitudinal wearable data reveal novel future health insightsSemantic proximity Read

An End-To-End Workflow for High-Throughput Discovery of Clinically Relevant Insights From Large Biomedical DatasetsSemantic proximity Read

Multiomics and digital monitoring during lifestyle changes reveal independent dimensions of human biology and healthSemantic proximity Read

The biological clock of multimorbidity: temporal dynamics of disease co-occurrence in primary careSemantic proximity Read