New algorithm improves gene expression marker identification across diverse biological systems
Researchers have developed a new computational approach that enables more accurate selection of genes that characterize different cellular states from mRNA-seq data, offering a more interpretable way ...
Researchers have developed a new computational approach that enables more accurate selection of genes that characterize different cellular states from mRNA-seq data, offering a more interpretable way to analyze complex biological data. The study, published in Frontiers in Immunology, involved researchers from the Germans Trias i Pujol Research Institute (IGTP), Universitat Politècnica de Catalunya (UPC), IrsiCaixa and the Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (CIBEREHD).
When cells respond to an infection, inflammation or a tumor, the activity of thousands of genes changes, altering their gene expression profiles. Analyzing this activity helps researchers understand how cells change state and adapt their function. However, conventional methods may fail to capture the full complexity of these processes, making their molecular characterization more difficult.
To address this limitation, the research team developed the Cartesian Distance-Based Gene Expression (CDBGE) approach, a novel algorithm designed to identify the genes that best distinguish between different biological conditions.
A broader view of gene activity
The method was evaluated using multiple publicly available datasets from human and mouse studies and across different experimental settings, demonstrating its ability to accurately classify samples and identify informative gene expression markers across diverse biological systems.
"Unlike conventional differential gene expression analyses, CDBGE integrates multidimensional and temporal information to better capture the complexity of biological behavior. This enables researchers to identify both well-established and previously unrecognized biomarkers, providing deeper insights into cellular heterogeneity and dynamic biological processes," said Qiaoling Ye, first author of the study and a predoctoral researcher with the Innate Immunity research group at IGTP and the Physics Department, Institute for Research and Innovation in Health (IRIS), at UPC.
"The method enhances the accuracy of gene selection, leading to improved biological classification while maintaining a simple, flexible and interpretable framework that can be applied to a wide range of experimental designs," she added.
Built on earlier macrophage work
CDBGE is based on a method originally developed to select genes differentially expressed across distinct in vitro-generated human macrophage phenotypes. By combining robustness, flexibility and interpretability, the approach represents a valuable new tool that could facilitate biomarker discovery and improve understanding of complex biological mechanisms.
Publication details
Qiaoling Ye et al, A versatile distance-based approach for gene expression selection across diverse biological systems, Frontiers in Immunology (2026). DOI: 10.3389/fimmu.2026.1843796
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Citation: New algorithm improves gene expression marker identification across diverse biological systems (2026, July 15) retrieved 16 July 2026 from https://phys.org/news/2026-07-algorithm-gene-marker-identification-diverse.html
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