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March 14, 2026Frontiers in GeneticsOpen Access

An in silico protocol for predicting genetic biomarkers in rare diseases: a case study in sporadic amyotrophic lateral sclerosis

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Authors

AAAli AguerdUniversity of Hassan II CasablancaBNBadreddine NouadiUniversity of Hassan II CasablancaAEAbdelkarim EzaouineUniversity of Hassan II Casablanca

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Implication

Machine learning uncovers new SNPs and therapeutic targets for rare diseases, suggesting effective analysis methods.

Key Points

  • The research aims to develop a machine-learning approach for predicting genetic biomarkers in rare diseases.
  • Developed a machine-learning protocol using Random Forest technique.
  • Trained the model on 189 known sALS-linked SNPs and 938,544 unrelated SNPs.
  • Analyzed genomic features to identify candidate SNPs.
  • Achieved 93.8% accuracy with near-perfect AUC scores.
  • Uncovered 1,890 new SNP candidates for sporadic amyotrophic lateral sclerosis.
  • Identified key genes linked to neural health as strong therapeutic targets.

Cite This Study

Aguerd et al. (2026) studied this question.

synapsesocial.com/papers/69b4ad7918185d8a39800ceahttps://doi.org/10.3389/fgene.2026.1742595
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