Article

Molecular Characterization of T-Lineage Acute Lymphoblastic Leukemia by an Optimal-Transport Based Multi-Omics Integration Framework

2026-05-26

Abstract excerpt

T-lineage acute lymphoblastic leukemia (T-ALL) is an aggressive pediatric malignancy characterized by complex heterogeneity across multiple molecular layers. Accurate subtyping is essential for understanding disease mechanisms, risk stratification, and guiding targeted therapeutic strategies. However, current diagnostic approaches are labor-intensive and time-consuming, and existing computational methods are limit...

Topics

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

Identifiers and source

Literature Corpus work: a6a94237-14df-530b-99c4-328d4c245980
DOI: 10.64898/2026.05.22.727257

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

Molecular Characterization of T-Lineage Acute Lymphoblastic Leukemia by an Optimal-Transport Based Multi-Omics Integration FrameworkDOI 10.64898/2026.05.22.727257Read

clinTALL: machine learning-driven multimodal subtype classification and treatment outcome prediction in pediatric T-ALLSemantic proximity Read

A novel deep optimal transport framework reveals prostate cancer risk heterogeneity, Alzheimer’s disease risk heterogeneity, and myeloma cells associated with a short-term sham bortezomib response and progressive diseaseSemantic proximity Read

Flexynesis: A deep learning framework for bulk multi-omics data integration for precision oncology and beyondSemantic proximity Read

Deep Learning Architectures for Multi-Omics Data Integration: Bridging Biomarker Discovery and Clinical TranslationSemantic proximity Read

TASC: A transcriptome-driven machine learning classifier to explore molecular heterogeneity and relapse-associated programs in T-cell Acute Lymphoblastic LeukemiaSemantic proximity Read

CHAMPOLLION: Robust Multi-Omics Integration via Inverse Optimal Transport Using Paired CellsSemantic proximity Read

Pathomic Fusion: An Integrated Framework for Fusing Histopathology and Genomic Features for Cancer Diagnosis and PrognosisSemantic proximity Read

Unifying multimodal single-cell data with a mixture-of-experts <i>β</i> -variational autoencoder frameworkSemantic proximity Read

Self-Normalizing Multi-Omics Neural Network for Pan-Cancer PrognosticationSemantic proximity Read

GAE-Δ: A Graph-Learning Framework for Gene Network Rewiring and Clinical Outcome Prediction from Multi-Omics DataSemantic proximity Read

Cooperative multi-view integration with Scalable and Interpretable Model ExplainerSemantic proximity Read

multiHIVE: Hierarchical Multimodal Deep Generative Modeling for Single-cell MultiomicsSemantic proximity Read