updated MOGDx Bioinf. paper

Author: tisimpson

_posts/2024-02-09-MOGDxNew.md

@@ -9,11 +9,10 @@ categories: research
 
 #### *Barry Ryan , Riccardo Marioni and T. Ian Simpson*
 
-
 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 missing data. In this study, we introduce Multi-Omic Graph Diagnosis (MOGDx), a flexible command line tool for the integration of multi-omic data to perform classification tasks for heterogeneous diseases. 
 
 MOGDx incorporates a network taxonomy for data integration and utilises a graph neural network architecture for classification. Networks con be easily integrated, can readily handle missing data, and have been used in a wide variety of biomedical applications in the unsupervised setting. Graph Neural Networks (GNN) have shown powerful classification performance on several benchmark network datasets. The use of GNN's in a supervised setting for disease classification is a promising avenue to redefine heterogenous diseases. 
 
 The performance of MOGDx was benchmarked on three distinct datasets from The Cancer Genome Atlas ([TCGA](https://www.cancer.gov/ccg/research/genome-sequencing/tcga)) for breast invasive carcinoma, kidney cancer, and low grade glioma. MOGDx demonstrated state-of-the-art performance and an ability to identify relevant multi-omic markers in each task. It did so while integrating more genomic measures with greater patient coverage compared to other network integrative methods. MOGDx is available to download from [Github](https://github.com/biomedicalinformaticsgroup/MOGDx). 
 
-For more information find the preprint to our paper online [here](https://www.medrxiv.org/content/10.1101/2023.07.09.23292410v2)
+Find the full paper in the journal [Bioinformatics](https://doi.org/10.1093/bioinformatics/btae523)