Article

Heterogeneity-Preserving Discriminative Feature Selection for Disease-Specific Subtype Discovery

2023-05-14

Abstract excerpt

The identification of disease-specific subtypes can provide valuable insights into disease progression and potential individualized therapies, important aspects of precision medicine given the complex nature of disease heterogeneity. The advent of high-throughput technologies has enabled the generation and analysis of various molecular data types, such as single-cell RNA-seq, proteomic, and imaging datasets, on a...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work: c8f732e0-5a1e-5a12-ba33-daed4efce1b4
DOI: 10.1101/2023.05.14.540686

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

Heterogeneity-Preserving Discriminative Feature Selection for Disease-Specific Subtype DiscoveryDOI 10.1101/2023.05.14.540686Read

Subtype-MGTP: a cancer subtype identification framework based on multi-omics translationSemantic proximity Read

Single-Cell Manifold Preserving Feature Selection (SCMER)Semantic proximity Read

Subtype-WESLR: identifying cancer subtype with weighted ensemble sparse latent representation of multi-view dataSemantic proximity Read

Single-Cell Manifold Preserving Feature Selection (SCMER)Semantic proximity Read

Single-cell phenotype-associated subpopulation identification via transfer foundation model and statistical ensemble learning.Semantic proximity Read

mcBERT: Patient-Level Single-cell Transcriptomics Data RepresentationSemantic proximity Read

TumorArchetypeR: A modular framework to derive signature-based tumor subtypesSemantic proximity Read

CLCluster: a redundancy-reduction contrastive learning-based clustering method of cancer subtype based on multi-omics dataSemantic proximity Read

MOT: a Multi-Omics Transformer for multiclass classification tumour types predictionsSemantic proximity Read

PIPET: predicting relevant subpopulations in single-cell data using phenotypic information from bulk data.Semantic proximity Read

Machine learning models reveal distinct disease subgroups and improve diagnostic and prognostic accuracy for individuals with pathogenic SCN8A gain-of-function variants.Semantic proximity Read

CancerSubminer: an integrated framework for cancer subtyping using supervised and unsupervised learning on DNA methylation profilesSemantic proximity Read