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Introducing feature identification and refinement engine (FIRE) for identifying consistent and informative gene
Shivam Kumar1, Devvrat Pandey1, Samrat Chatterjee1
1Complex Analysis Group, Computational and Mathematical Biology Centre, Translational Health Science and Technology Institute, NCR Biotech Science Cluster, Faridabad, 121001, India.
Computational Biology and Chemistry
|November 2, 2025
Summary
We developed a machine learning framework, FIRE, to identify molecular signatures for diseases like glioblastoma. FIRE successfully pinpointed 33 genes distinguishing glioblastoma, offering a promising tool for cancer research and diagnostics.
Area of Science:
- Biomedical research
- Computational biology
- Genomics
Background:
- Identifying molecular signatures for diseases is challenging due to data complexity and heterogeneity.
- Glioblastoma, an aggressive brain tumor, suffers from molecular heterogeneity, complicating clinical management.
- Existing methods often struggle with high data variance and complex biological patterns.
Purpose of the Study:
- To present FIRE (Feature Identification and Refinement Engine), a novel machine learning framework for integrative omics data analysis.
- To enable robust extraction of biologically meaningful features from complex, heterogeneous datasets.
- To identify a consistent and reproducible molecular signature for glioblastoma.
Main Methods:
- FIRE utilizes data merging and an ensemble machine learning approach.
- The framework detects both linear and non-linear patterns across diverse omics datasets.
- Repeated cross-validation across multiple independent datasets was employed to establish robustness.
Main Results:
- FIRE identified 33 genes that consistently distinguish glioblastoma from control samples.
- Several identified genes are associated with established cancer hallmarks.
- FIRE demonstrated superior predictive performance compared to existing glioblastoma signatures.
Conclusions:
- FIRE is a robust machine learning framework for feature identification and refinement in omics data.
- The identified gene set provides a promising molecular signature for glioblastoma.
- FIRE shows potential as a versatile tool applicable to other complex diseases.
