NetAurHPD: Network Auralization Hyperlink Prediction Model to Identify Metabolic Pathways from Metabolomics Data

TL;DR

NetAurHPD is a novel framework that combines graph auralization and data augmentation to predict metabolic pathways from metabolite correlation networks, especially effective with sparse data.

Contribution

The paper introduces NetAurHPD, a new method that enhances pathway prediction accuracy using graph auralization and data augmentation techniques in metabolomics.

Findings

NetAurHPD outperforms existing methods in challenging conditions.

Data augmentation improves model performance.

Promising results on tomato pericarp metabolite networks.

Abstract

Metabolite biosynthesis is regulated via metabolic pathways, which can be activated and deactivated within organisms. Understanding and identifying an organism's metabolic pathway network is a crucial aspect for various research fields, including crop and life stock breeding, pharmacology, and medicine. The problem of identifying whether a pathway is part of a studied metabolic system is commonly framed as a hyperlink prediction problem. The most important challenge in prediction of metabolic pathways is the sparsity of the labeled data. This challenge can partially be mitigated using metabolite correlation networks which are affected by all active pathways including those that were not confirmed yet in laboratory experiments. Unfortunately, extracting properties that can confirm or refute existence of a metabolic pathway in a particular organism is not a trivial task. In this research, we introduce the Network Auralization Hyperlink Prediction (NetAurHPD) which is a framework that relies on (1) graph auralization to extract and aggregate representations of nodes in metabolite correlation networks and (2) data augmentation method that generates metabolite correlation networks given a subset of chemical reactions defined as hyperlinks. Experiments with metabolites correlation-based networks of tomato pericarp demonstrate promising results for NetAurHPD, compared to alternative methods. Furthermore, the application of data augmentation improved NetAurHPD's learning capabilities and overall performance. Additionally, NetAurHPD outperformed state-of-the-art method in experiments under challenging conditions, and has the potential to be a valuable tool for exploring organisms with limited existing knowledge.