Article

Multivariate Random Forests for Cross-Modal Multi-Omics Integration

2026-06-22

Abstract excerpt

Multi-omics studies are widely used across many areas of biomedical research. In many diseases, some signals are shared across data types, while others are strongest in a single omics layer. Current multi-omics clustering methods often either merge all data types into a single representation, which can blur biology that is strong in one layer, or rely on linear structure that may miss more complex relationships ac...

Topics

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

Identifiers and source

Literature Corpus work: 84964adc-5cc6-561f-83ef-2306eff954d3
DOI: 10.64898/2026.06.17.732933

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

Multivariate Random Forests for Cross-Modal Multi-Omics IntegrationDOI 10.64898/2026.06.17.732933Read

X-intNMF: A Cross- and Intra-Omics Regularized NMF Framework for Multi-Omics IntegrationSemantic proximity Read

Multi-Omics factor analysis - a framework for unsupervised integration of multi-omic data setsSemantic proximity Read

An Integrative Multi-Omics Random Forest Framework for Robust Biomarker DiscoverySemantic proximity Read

PIMO: Pathway-based Interpretable Multi-Omics interactions for multi-omics integrationSemantic proximity Read

Identification of Cancer Risk Groups through Multi-Omics Integration using Autoencoder and Tensor AnalysisSemantic proximity Read

Analyzing associations and higher-order effects in multi-omics data with double machine learningSemantic proximity Read

MOSAIC: A Spectral Framework for Integrative Phenotypic Characterization Using Population-Level Single-Cell Multi-OmicsSemantic proximity Read

A semi-supervised approach for the integration of multi-omics data based on transformer multi-head self-attention mechanism and graph convolutional networksSemantic proximity Read

WIMOAD: Weighted Integration of Multi-Omics Data with Meta Learning for Alzheimer’s Disease DiagnosisSemantic proximity Read

Multi-‘Omic Integration via Similarity Network Fusion to Detect Molecular Subtypes of AgingSemantic proximity Read

Balanced deep learning on multi-omics networks identifies molecular subgroups of pathological brain agingSemantic proximity Read

Mutual information for detecting multi-class biomarkers when integrating multiple bulk or single-cell transcriptomic studiesSemantic proximity Read