Unsupervised Clustering and Interactive Visualization for Glycan Data Analysis

Diplôme Master Unsupervised Clustering and Interactive Visualization for Glycan Data Analysis eng 2025-04-09 author Ceylan Fatma Elif 0009-0008-5230-7617 824739 director Lisacek Frédérique 0000-0002-0948-4537 639315 director Hayes Catherine 810429 62 Clustering is a fundamental task in data science, enabling the discovery of hidden structures in large datasets. In glycoinformatics, the classification and analysis of glycans present unique computational challenges due to their highly branched structures and compositional ambiguity, making conventional sequence-based approaches insufficient. This thesis explores an unsupervised clustering approach tailored for glycan data, integrating computational techniques to improve glycan classification and visualization. We propose a multi-step clustering methodology implemented in Python, leveraging DBSCAN (Density-Based Spatial Clustering of Applications with Noise) with iterative refinements to enhance cluster accuracy. By incorporating domain-specific reassignment rules and substructure-based adjustments, the approach ensures that structurally similar glycans are grouped meaningfully. The results are visualized through an interactive D3.js Sunburst chart, providing an accessible and intuitive representation of glycan clusters. The proposed framework was evaluated using data from GlyConnect, demonstrating its effectiveness in uncovering meaningful glycan relationships. However, challenges such as computational efficiency, automated validation, and scalability remain open for further research. Future work will explore deep learning-based clustering techniques, including Graph Neural Networks (GNNs), to enhance the clustering of glycans with complex structural variations. By bridging computational clustering with glycoinformatics, this work contributes to the broader field of data science, emphasizing the role of unsupervised learning in biological data analysis Data Science Unsupervised Clustering DBSCAN Python Cluster visualization Bioinformatics Glycoinformatics Glycobiology Glycans GlyConnect Dewey 025.063 Master en Sciences Informatiques 13 Centre universitaire d'informatique 0D3 32 Département d'informatique 18 false 1316 Proteome informatics Group c1dfd448-0c84-484d-9955-852fa16dd342 Master thesis Restricted CEYLAN_Master_Thesis.pdf 4561640 application/pdf 1db5ed7ad0db0e14f9e7618ba07a510043776f6094f24f944347cee1f3980247 Vignette Public CEYLAN_Abstract.jpg 153121 image/png restrictedAccess Centres et instituts / Centre universitaire d'informatique Faculté des sciences / Département d'informatique

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Unsupervised Clustering and Interactive Visualization for Glycan Data Analysis				2025	48	6

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Ceylan, Fatma Elif

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