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Related Experiment Video

Updated: May 24, 2026

Novel Atomic Force Microscopy Based Biopanning for Isolation of Morphology Specific Reagents against TDP-43 Variants in Amyotrophic Lateral Sclerosis
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Novel Atomic Force Microscopy Based Biopanning for Isolation of Morphology Specific Reagents against TDP-43 Variants in Amyotrophic Lateral Sclerosis

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A Consensus Clustering Approach to Amyotrophic Lateral Sclerosis Phenotyping.

Pilar M Ferraro1, Sara Narteni2, Marta Lenatti2

  • 1IRCCS Ospedale Policlinico San Martino, 16152, Genoa, Italy.

Studies in Health Technology and Informatics
|May 23, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces a data-driven approach to characterize Amyotrophic Lateral Sclerosis (ALS) phenotypes using clinical data. Findings reveal distinct patient clusters, aiding in a better understanding of ALS heterogeneity.

Keywords:
consensus clusteringdisease phenotypingmotoneuron diseases

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Area of Science:

  • Neurology
  • Data Science
  • Biostatistics

Background:

  • Amyotrophic Lateral Sclerosis (ALS) presents significant phenotyping challenges due to its heterogeneity and rarity.
  • Accurate phenotyping is crucial for understanding disease progression and developing targeted therapies.

Purpose of the Study:

  • To develop and validate a data-driven methodology for characterizing ALS phenotypes.
  • To investigate the alignment of data-derived patient clusters with established clinical phenotypes and medical knowledge.

Main Methods:

  • A consensus clustering approach was employed, analyzing clinical data from a comprehensive battery of examinations.
  • The method involved creating stable clusters across multiple random data sub-samples to ensure robustness.

Main Results:

  • Consistent patient profiles were identified for bulbar onset ALS, primarily defined by onset characteristics.
  • Spinal onset ALS patients demonstrated greater heterogeneity within their identified phenotype clusters.

Conclusions:

  • The proposed data-driven approach effectively supports ALS phenotyping by identifying distinct patient subgroups.
  • Results highlight differential heterogeneity between bulbar and spinal onset ALS, offering insights for future research and clinical stratification.