HyperTMO: a trusted multi-omics integration framework based on hypergraph convolutional network for patient classification.
Bioinformatics
; 40(4)2024 Mar 29.
Article
in En
| MEDLINE
| ID: mdl-38530977
ABSTRACT
MOTIVATION The rapid development of high-throughput biomedical technologies can provide researchers with detailed multi-omics data. The multi-omics integrated analysis approach based on machine learning contributes a more comprehensive perspective to human disease research. However, there are still significant challenges in representing single-omics data and integrating multi-omics information. RESULTS:
This article presents HyperTMO, a Trusted Multi-Omics integration framework based on Hypergraph convolutional network for patient classification. HyperTMO constructs hypergraph structures to represent the association between samples in single-omics data, then evidence extraction is performed by hypergraph convolutional network, and multi-omics information is integrated at an evidence level. Last, we experimentally demonstrate that HyperTMO outperforms other state-of-the-art methods in breast cancer subtype classification and Alzheimer's disease classification tasks using multi-omics data from TCGA (BRCA) and ROSMAP datasets. Importantly, HyperTMO is the first attempt to integrate hypergraph structure, evidence theory, and multi-omics integration for patient classification. Its accurate and robust properties bring great potential for applications in clinical diagnosis. AVAILABILITY AND IMPLEMENTATION HyperTMO and datasets are publicly available at https//github.com/ippousyuga/HyperTMO.
Full text:
1
Database:
MEDLINE
Main subject:
Breast Neoplasms
/
Alzheimer Disease
Limits:
Female
/
Humans
Language:
En
Year:
2024
Type:
Article