Article

Multi-Omic Graph Diagnosis (MOGDx) : A data integration tool to perform classification tasks for heterogeneous diseases

2023-07-09

Abstract excerpt

Heterogeneity in human diseases presents challenges in diagnosis and treatments due to the broad range of manifestations and symptoms. With the rapid development of labelled multi-omic data, integrative machine learning methods have achieved breakthroughs in treatments by redefining these diseases at a more granular level. These approaches often have limitations in scalability, oversimplification, and handling of...

Topics

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

Identifiers and source

Literature Corpus work: d19505ca-3989-5179-9af4-9fafcbfa976b
DOI: 10.1101/2023.07.09.23292410

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

Multi-Omic Graph Diagnosis (MOGDx) : A data integration tool to perform classification tasks for heterogeneous diseasesDOI 10.1101/2023.07.09.23292410Read

IGCN: integrative graph convolution networks for patient level insights and biomarker discovery in multi-omics integrationSemantic proximity Read

PEARL: Integrative multi-omics classification and omics feature discovery via deep graph learningSemantic proximity Read

MOGAT: An Improved Multi-Omics Integration Framework Using Graph Attention NetworksSemantic proximity Read

MoAGL-SA: a multi-omics adaptive integration method with graph learning and self attention for cancer subtype classificationSemantic proximity Read

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

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

DeepMOCCA: A pan-cancer prognostic model identifies personalized prognostic markers through graph attention and multi-omics data integrationSemantic proximity Read

SUPREME: A cancer subtype prediction methodology integrating multiomics data using Graph Convolutional Neural NetworkSemantic proximity Read

LaCONIC: A Label-Aware and Graph-Guided Contrastive Multi-Omics Collaborative Learning Model for Cancer Risk PredictionSemantic proximity Read

COmic: convolutional kernel networks for interpretable end-to-end learning on (multi-)omics data.Semantic proximity Read

Multi-omics integration and regulatory inference for unpaired single-cell data with a graph-linked unified embedding frameworkSemantic proximity Read