A Machine Learning Approach for Highlighting microRNAs as Biomarkers Linked to Amyotrophic Lateral Sclerosis...

Area of Science:

• Neuroscience
• Biomarker Discovery
• Bioinformatics

Background:

• Amyotrophic Lateral Sclerosis (ALS) is a fatal neurodegenerative disease.
• Early ALS diagnosis is challenging, relying on clinical assessment and exclusion of other conditions.
• MicroRNA (miRNA) expression profiles in biofluids offer potential as non-invasive biomarkers.

Purpose of the Study:

• To synthesize current knowledge on miRNA biomarkers for ALS.
• To address controversial findings in existing scientific literature.
• To utilize a machine-learning approach for a comprehensive analysis.

Main Methods:

• Systematic literature search of 308 articles.
• Analysis using the MySLR digital platform.
• Latent Dirichlet Allocation (LDA) algorithm for topic modeling.

Main Results:

• Two relevant research topics concerning miRNAs in ALS were identified.
• Analysis covered biomolecular mechanisms, translational, and clinical aspects.
• Specific miRNAs in tissues, blood, and cerebrospinal fluid (CSF) linked to ALS diagnosis and progression.

Conclusions:

• Certain miRNAs show potential as non-invasive biomarkers for ALS.
• The study provides insights into the current state of miRNA research in ALS.
• Future research priorities and goals for ALS biomarker development were proposed.