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Updated: Jan 6, 2026

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

Dynamic sensor selection for biomarker discovery.

Joshua Pickard¹, Cooper Stansbury¹, Amit Surana²

¹Department of Computational Medicine & Bioinformatics, University of Michigan, Ann Arbor, MI 48109.

Proceedings of the National Academy of Sciences of the United States of America

|October 7, 2025

Summary

This summary is machine-generated.

Observability theory offers a new method for selecting biological markers (biomarkers) from complex data. This approach identifies meaningful biological sensors across various applications, from biomanufacturing to neural systems.

Keywords:

biomarkers data driven observability dynamic sensor selection observability sensor selection

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Area of Science:

Systems Biology
Biotechnology
Data Science

Background:

Biotechnologies allow high-resolution biological system monitoring.
Identifying relevant biomarkers from large datasets is challenging.
Classical biomarker selection methods struggle with complex biological data.

Purpose of the Study:

To develop a general methodology for biomarker selection using observability theory.
To identify biologically meaningful sensors in time-series data.
To extend biomarker discovery to multiple data modalities and dynamic systems.

Main Methods:

Application of observability theory for biomarker selection.
Introduction of dynamic sensor selection to adapt to changing system dynamics.
Integration of transcriptomics and chromosome conformation data.
Evaluation using neural activity data (movies, EEG).

Main Results:

Observability successfully identified biologically meaningful sensors in transcriptomics data.
Dynamic sensor selection enhanced observability in changing biological regimes.
The framework demonstrated broad applicability across diverse data types and systems.
Successful application to agricultural, biomanufacturing, and neural system data.

Conclusions:

Observability theory provides a robust framework for biomarker discovery.
The dynamic sensor selection method addresses biological system variability.
This approach offers a versatile tool for biomarker identification in various scientific fields.