Local graph estimation with pathwise false discovery control

2026-05-12 · Source: Machine learning : nature.com subject feeds · Field: Science & Research — Mathematics & Computational Sciences, Life Sciences & Biology, Health & Medical Research · Depth: Expert, quick

Summary

Researchers introduce local graph estimation, a statistical framework designed to infer substructures around specific target variables within complex datasets. This approach addresses the limitation of traditional graph estimation methods, which often obscure local relationships when attempting to model full inter-variable networks. The proposed solution, pathwise feature selection (PFS), iteratively applies feature selection and propagates uncertainty along network paths. PFS provides rigorous finite-sample false discovery control, even with mixed variable types and nonlinear dependencies. The framework was successfully applied across four distinct domains: environmental and public health, multiomics, brain connectomics, and single-nucleus RNA sequencing, where it recovered interpretable networks consistent with existing domain knowledge and generated novel hypotheses.

Key takeaway

For AI scientists analyzing complex biological or environmental datasets with specific target variables, traditional full-network estimation may mask crucial local interactions. You should consider implementing local graph estimation with pathwise feature selection (PFS) to uncover interpretable substructures and generate robust hypotheses, especially in settings with diverse variable types and nonlinear dependencies.

Key insights

Local graph estimation with pathwise feature selection (PFS) infers interpretable substructures around target variables.

Principles

• Full network estimation can obscure local structures.
• Uncertainty propagation improves false discovery control.

Method

PFS estimates local subgraphs by iteratively applying feature selection and propagating uncertainty along network paths to control false discoveries.

In practice

• Apply PFS to analyze biomarker relationships.
• Use PFS for brain connectomics studies.
• Employ PFS in single-nucleus RNA sequencing.

Topics

• Local Graph Estimation
• Pathwise Feature Selection
• False Discovery Control
• Multiomics
• Brain Connectomics

Best for: AI Scientist, Research Scientist, Data Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by Machine learning : nature.com subject feeds.