Discover Community Create You

Loading publication...

Noodle always counts identifier-free feature events for service operation. With your permission, Google Analytics can also estimate audience and engagement. Search text, publication identifiers, cookies, IP addresses and browser fingerprints are never included in Noodle's first-party event counters.

Privacy details

Article

A Consensus Clustering Approach to Amyotrophic Lateral Sclerosis Phenotyping.

Studies in health technology and informatics - 21 May 2026

Ferraro Pilar M, Narteni Sara, Lenatti Marta, Oliveri Francesca, Gemelli Chiara, Cabona Corrado, Uccelli Antonio, Paglialonga Alessia, Mongelli Maurizio, Schenone Angelo

Abstract excerpt

Amyotrophic Lateral Sclerosis (ALS) phenotyping is a challenging task due to its heterogeneous nature and low prevalence. In this paper, we introduce a data-driven approach to support the characterization of ALS phenotypes based on clinical data from a battery of examinations. A consensus clustering method is proposed to identify stable clusters across multiple random data sub-samples, with the objective of...

Read the complete abstract on PubMed

Topics

Share this publication in a Topic to start or enrich a Post.

Share to Amyotrophic Lateral Sclerosis
Share to Cluster Analysis

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Share to Clustering Algorithms

Share to Phenotype

Source

Identifiers

PMID: 42174849
DOI: 10.3233/SHTI260173

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 8 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

A Consensus Clustering Approach to Amyotrophic Lateral Sclerosis Phenotyping.PMID 42174849Read ↗

Unsupervised machine learning identifies distinct molecular and phenotypic ALS subtypes in post-mortem motor cortex and blood expression dataSemantic proximity Read ↗

Unraveling the Heterogeneity of ALS—A Call to Redefine Patient Stratification for Better Outcomes in Clinical TrialsSemantic proximity Read ↗

Unsupervised machine-learning identifies clinically distinct subtypes of ALS that reflect different genetic architectures and biological mechanismsSemantic proximity Read ↗

Latent cluster analysis of ALS phenotypes identifies prognostically differing groups.Semantic proximity Read ↗

Brain metabolic imaging with 18 F-PET-CT and machine-learning clustering analysis reveal divergent metabolic phenotypes in patients with amyotrophic lateral sclerosis.Semantic proximity Read ↗

The ALS Research Collaborative: A Long Running Multimodal ALS Natural History ResourceSemantic proximity Read ↗

Identification and prediction of ALS subgroups using machine learningSemantic proximity Read ↗

Imaging-derived neuromuscular ultrasound phenotypes are associated with functional status in amyotrophic lateral sclerosis.Semantic proximity Read ↗

An unsupervised machine learning method for discovering patient clusters based on genetic signatures.Semantic proximity Read ↗

Multidimensional knowledge-based framework is an essential step in the categorization of gene sets in complex disorders.Semantic proximity Read ↗

Symptom Clustering of Dysphagia Phenotypes by Unsupervised Machine Learning Using Multidimensional Characteristics: A Cross-sectional Study of the Elderly Chinese PopulationSemantic proximity Read ↗

Analysis of terms used for the diagnosis and classification of amyotrophic lateral sclerosis and motor neuron disease.Semantic proximity Read ↗

⌁Select a neighboring publication to make it the new centre.