HyperTMO: a trusted multi-omics integration framework based on hypergraph convolutional network for patient classification

• DOI: 10.1093/bioinformatics/btae159
• Publication Date: 2024-03-26T20:05:44Z
• Authors: Haohua Wang; Kai Lin; Qiang Zhang; Jinlong Shi; Xinyu Song; Jue Wu; Chenghui Zhao; Kunlun He
• Keywords: Machine Learning; 0301 basic medicine; Original Paper; 03 medical and health sciences; Alzheimer Disease; Humans; Female; Breast Neoplasms; Breast; Multiomics

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