# SynOmics: integrating multi-omics data through feature interaction networks

## TL;DR

SynOmics is a new framework that improves multi-omics data integration using graph networks, leading to better biomedical predictions.

## Contribution

Introduces SynOmics, a graph convolutional network framework that models both within- and cross-omics feature interactions for improved integration.

## Key findings

- SynOmics outperforms existing methods in multi-omics integration across biomedical classification tasks.
- The framework captures both intra-omics and inter-omics dependencies through parallel learning.
- It shows potential for biomarker discovery and clinical applications.

## Abstract

The integration of multi-omics data is essential for achieving a comprehensive understanding of molecular systems and enhancing the performance of predictive models in biomedical research. However, many existing models have limited capacity to capture cross-omics feature interactions, which hinders the depth of integration. In this study, we introduce SynOmics, a graph convolutional network framework designed to improve multi-omics integration by constructing omics networks in the feature space and modeling both within- and cross-omics dependencies. By incorporating both omics-specific networks and cross-omics bipartite networks, SynOmics enables simultaneous learning of intra-omics and inter-omics relationships. Unlike traditional approaches that rely on early or late integration strategies, SynOmics adopts a parallel learning strategy to process feature-level interactions at each layer of the model. Experimental results demonstrate that SynOmics consistently outperforms state-of-the-art multi-omics integration methods across a range of biomedical classification tasks, highlighting its potential for biomarker discovery and clinical applications.