Dynamic sensor selection for biomarker discovery.

ABSTRACT: Recent advances in biotechnologies enable monitoring of biological systems with unprecedented resolution, yet identifying and interpreting biological signals remains a major challenge in clinical and research settings. Classically, biomarkers are measurable indicators of the state of biological processes. Given the large number of molecules in modern datasets, a major challenge is identifying the best biomarkers for a particular setting. Here, we apply observability theory to establish a general methodology for biomarker selection. We demonstrate that observability identifies biologically meaningful sensors in a range of time series transcriptomics data. To address unique biological constraints, we introduce the method of dynamic sensor selection to maximize observability over time, thus e

SUBMITTER: Pickard J 

PROVIDER: S-EPMC12541339 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature