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Identification of Reliable Biomarkers for ALS Through Machine Learning Approach.

Renu Yadav¹, Pragya Pragya¹, Jac Fredo Agastinose Ronickom¹

¹Computational Neuroscience and Biology Lab, School of Biomedical Engineering, Indian Institute of Technology (BHU) Varanasi, Uttar Pradesh, India.

Studies in Health Technology and Informatics

|May 23, 2026

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Machine learning models identified key genes, including Decorin (DCN), as potential biomarkers for Amyotrophic Lateral Sclerosis (ALS). This approach enhances diagnostic and therapeutic strategies for this neurodegenerative disease.

Area of Science:

Neuroscience
Bioinformatics
Genomics

Background:

Amyotrophic Lateral Sclerosis (ALS) is a fatal neurodegenerative disorder with progressive motor neuron degeneration.
Limited diagnostic biomarkers and disease heterogeneity present challenges in ALS research.
Identifying robust molecular biomarkers is crucial for early diagnosis and targeted therapies.

Purpose of the Study:

To develop a machine learning (ML) pipeline integrating transcriptome data for identifying potential ALS biomarkers.
To leverage feature selection techniques to pinpoint key genes from high-dimensional gene expression data.
To evaluate the diagnostic potential of identified genes in ALS.

Main Methods:

Utilized RNA-Seq data from ALS patients and healthy controls obtained from public GEO datasets.

Keywords:

Amyotrophic Lateral Sclerosis Biomarkers Feature Importance Machine learning Transcriptomic data

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Published on: December 15, 2023

Implemented ensemble ML models, specifically eXtreme Gradient Boosting (XGBoost) and Random Forest (RF), with five-fold cross-validation.

Applied feature importance ranking to identify top candidate genes from the ML models.

Main Results:

Identified top 10 differentially expressed genes using XGBoost and RF models.
The Decorin (DCN) gene was consistently ranked among the top features by both models, indicating stability and relevance.
Achieved high classification performance: RF (98.8% accuracy) and XGBoost (97.6% accuracy), with excellent sensitivity, specificity, precision, and F1-scores.

Conclusions:

Transcriptomic data combined with ML effectively identifies potential ALS biomarkers.
The Decorin (DCN) gene shows promise as a stable and biologically relevant biomarker for ALS.
This approach offers utility for developing diagnostic and therapeutic strategies for Amyotrophic Lateral Sclerosis.