Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Article Faculty Publications -Articles Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Moozhippurath, Bineesh; Natarajan, Jayapandian Title A name given to the resource Metabolomics Pathway Prediction Using Enhanced-Graph Convolutional Networks with Graph Attention Networks Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived International Journal of Online and Biomedical Engineering;Volume;21;Issue;10;pp.48-62 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.3991/ijoe.v21i10.55539" target="_blank" rel="noreferrer noopener">https://doi.org/10.3991/ijoe.v21i10.55539</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105014654585?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105014654585?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Moozhippurath B., Department of Computer Science and Engineering, Christ University, Karnataka, Bangalore, India; Natarajan J., Department of Computer Science and Engineering, Christ University, Karnataka, Bangalore, India Description An account of the resource Metabolomics, the comprehensive study of small molecules in biological systems, has a central role to play in the diagnosis of diseases, biomarker detection, and the design of new drugs. Although there have been major breakthroughs in analytical toolsets such as mass spectrometry (MS) coupled with chromatography, it is hard to predict metabolomics pathways because biochemical interactions are inherently complex. To meet this end, the current research suggests a deep learning-based approach using graph neural networks (GNN), which have shown high efficiency for graph-structured biological data. We specifically propose an enhanced graph convolutional network integrated with graph attention networks (EGCNGAT) to enhance pathway prediction performance. The hybrid framework employs graph convolutional networks (GCN) to represent molecular structural data and graph attention networks (GAT) to provide context-sensitive feature importance, thus improving the models capacity for learning complex pathway patterns. Comparative experiments against current deep learning approaches show that the introduced EGCN-GAT model obtains an accuracy of 98.90 percent, which is a 0.26 percent increase compared to the baseline MLGL-MP model. In addition, it demonstrates a 0.94 percent gain in precision as well as a slight gain in recall. The findings validate the performance of the proposed method and highlight its utility for developing pathway-level predictions in metabolomics studies. 2025 by the authors of this article. Published under CC-BY. Subject The topic of the resource bioinformatics; deep learning; graph convolutional network (GCN); graph neural network (GNN); metabolomics; prediction Publisher An entity responsible for making the resource available International Federation of Engineering Education Societies (IFEES) Relation A related resource ISSN: 26268493; Language A language of the resource English Type The nature or genre of the resource Article Rights Information about rights held in and over the resource All Open Access; Gold Open Access Format The file format, physical medium, or dimensions of the resource online