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Frequency-dependent Selection

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Related Experiment Video

Updated: Sep 15, 2025

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

Uncovering Heterogeneous Effects via Localized Feature Selection.

Xiaoxia Liu¹, Jiaqi Gu¹, Zhaomeng Chen²

¹Department of Neurology and Neurological Sciences, Stanford University, Stanford, CA 94305, USA.

Biorxiv : the Preprint Server for Biology

|July 15, 2025

Summary

This summary is machine-generated.

This study introduces a new framework for precision medicine, identifying individual disease features using localized statistics and knockoffs for replicability. It uncovers hidden heterogeneity and enables powerful population-level analysis for targeted therapies.

Keywords:

Precision medicine explainable artificial intelligence false discovery rate knockoffs localized feature selection replicability

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Related Experiment Videos

Last Updated: Sep 15, 2025

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

Experimental Protocol for Manipulating Plant-induced Soil Heterogeneity

Experimental Protocol for Manipulating Plant-induced Soil Heterogeneity

Published on: March 13, 2014

Heterogeneity Mapping of Protein Expression in Tumors using Quantitative Immunofluorescence

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Published on: October 25, 2011

Area of Science:

Computational Biology
Genomics
Biostatistics

Background:

Precision medicine requires identifying disease-associated features while accounting for population heterogeneity.
Current methods often overlook individual-level variations, focusing on population or subpopulation analyses.
Vast health datasets exist but are underutilized for capturing individual differences in disease triggers.

Purpose of the Study:

To develop a novel framework for identifying disease-associated features tailored to individual profiles.
To address the limitation of overlooking individual-level heterogeneity in disease research.
To enable the discovery of hidden heterogeneous effects within complex health data.

Main Methods:

Utilized localized test statistics to identify individual-specific disease features.
Employed the knockoffs methodology to control noise and ensure replicability of feature selection.
Applied the framework to single-cell RNA sequencing data for Alzheimer's disease.

Main Results:

Successfully identified disease-associated features at the individual level, accounting for heterogeneity.
Demonstrated the discovery of hidden heterogeneous effects in Alzheimer's disease data.
Showcased the framework's ability to aggregate results for powerful population-level feature selection.

Conclusions:

The proposed framework is a powerful tool for exploratory precision medicine studies.
It offers potential for generating novel hypotheses for biological validation.
The method enhances understanding of individual disease variations and targeted treatment strategies.