Article

Weakly supervised learning uncovers phenotypic signatures in single-cell data

2024-07-29

Abstract excerpt

To deliver clinically relevant insights from large patient cohorts profiled with single-cell technologies, a key challenge is to relate sample-level and single-cell measurements. We present MultiMIL, a deep learning framework that applies attention-based multiple-instance learning for phenotype prediction and cell state identification. We applied MultiMIL to peripheral blood mononuclear cells from COVID-19 patient...

Topics

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

Identifiers and source

Literature Corpus work: 42c52318-b574-5253-b617-aac280b5d787
DOI: 10.1101/2024.07.29.605625

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

Weakly supervised learning uncovers phenotypic signatures in single-cell dataDOI 10.1101/2024.07.29.605625Read

Exploring phenotype-related single-cells through attention-enhanced representation learning.Semantic proximity Read

Exploring phenotype-related single-cells through attention-enhanced representation learningSemantic proximity Read

scMILD: Single-cell Multiple Instance Learning for Sample Classification and Associated Subpopulation DiscoverySemantic proximity Read

Incorporating Hierarchical Information into Multiple Instance Learning for Patient Phenotype Prediction with scRNA-seq DataSemantic proximity Read

Application and Characterization of the Multiple Instance Learning Framework in Flow CytometrySemantic proximity Read

Learning multi-cellular representations of single-cell transcriptomics data enables characterization of patient-level disease statesSemantic proximity Read

Clinical Phenotype Prediction From Single-cell RNA-seq Data using Attention-Based Neural NetworksSemantic proximity Read

Annotation-free phenotype prediction using knowledge-augmented clustering from single-cell RNA sequencing data.Semantic proximity Read

Deep interpretable learning of sample representations for characterizing disease states in single-cell transcriptomicsSemantic proximity Read

Representation learning enables robust single cell phenotyping in whole slide liquid biopsy imaging.Semantic proximity Read

Contrastive Representation Learning for Single Cell Phenotyping in Whole Slide Imaging of Enrichment-free Liquid BiopsySemantic proximity Read

CloudPred: Predicting Patient Phenotypes From Single-cell RNA-seq.Semantic proximity Read