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 Conference Paper Faculty Publications- Conference Papers 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 Enhancing Metabolomics Pathway Prediction with Sequential Graph Convolutional Network 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 2025 IEEE 14th International Conference on Communication Systems and Network Technologies, CSNT 2025;pp.733-737 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/CSNT64827.2025.10967932" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/CSNT64827.2025.10967932</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105004556930?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105004556930?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, Kengeri Campus, Bangalore, India; Natarajan J., Department of Computer Science and Engineering, Christ University, Kengeri Campus, Bangalore, India Description An account of the resource Metabolomics is a powerful tool for the understanding of biological systems by analysis of metabolites and their related pathways. Prediction of metabolic pathways is still one of the most challenging tasks because of the complexity of molecular structures and graph-structured metabolomics data. This article presents a robust framework using Graph Convolutional Networks (GCNs) to address the challenges. The methodology proposed includes first preprocessing through metabolite identification by mass spectrometry, and then it utilizes feature extraction through the RDKit library. The objective of the research is aim to metabolic pathway prediction using machine learning algorithm. Complex patterns and relationships are captured from the SMILES representation through the molecular graphs constructed and passed on for the GCN model to learn structured data. ReLU activation functions have been employed within a three-layer sequential GCN architecture that enables it to deliver highly accurate results while ensuring that they are understandable as well. The proposed sequential GCN Model was evaluated on the KEGG dataset with an accuracy of 98.00%, precision of 92.10%, and recall of 93.02%. The performance of these metrics is well beyond traditional approaches such as KNN, ensemble logistic regression, and other GCN based approaches. Thus, this work brings GCN based approaches closer to revolutionizing metabolic pathway prediction and the advancement of the metabolomics field. 2025 IEEE. Subject The topic of the resource Biochemical; Deep learning; Graph Convolutional Networks; Machine Learning; Metabolomics Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-833153193-5; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online