http://repository.iiitd.edu.in/xmlui/handle/123456789/1721 Year-2024 2026-09-20T14:40:58Z http://repository.iiitd.edu.in/xmlui/handle/123456789/2140 Evaluating the quality of ontologies Sammi, Daksh; Bhushan, Lakshay; Mutharaju, Raghava (Advisor); Shimizu, Cogan (Advisor) Ontologies serve as fundamental tools for organizing and representing knowledge in various do mains. Evaluating ontology quality is crucial for ensuring their e!ectiveness and usability. This paper presents a comprehensive review of ontology evaluation framework, encompassing both traditional approaches and emerging techniques. Method-based evaluation, focusing on criteria such as syntax, structure, and semantics, o!ers insights into ontology design and construction. Additionally, level-based evaluation divides assessment into distinct layers, enhancing the un derstanding of ontology characteristics. Our tool will combine the major frameworks used by knowledge experts to evaluate their ontologies like OQuaRE, OntoQA, Orme et. al., etc., which are selected after several literature reviews about their use cases. Our tool is complemented with an additional layer of LLMs suggesting knowledge experts on how to improve their ontol ogy based on the calculated metrics and o!ering in-depth insights from each calculated metric. The LLMs in our tool are connected via a retrieval augmentation pipeline. Our tool is built to overcome the limitations of pre-exisitng tools which are either not working, not available, do not take all major frameworks into account or do not provide insights regarding calculated metrics for the knowledge expert to improve their ontology. Our paper concludes with insights into future research directions and the importance of adopting a holistic approach to ontology evaluation. 2024-11-27T00:00:00Z http://repository.iiitd.edu.in/xmlui/handle/123456789/2139 Cryo-EM resolution improvement through enhanced deep learning methods. Jain, Prem Kamal; Garg, Manan; Jain, Vivek; Kumar, Vibhor (Advisor) Recent advancements in cryo-electron microscopy (cryo-EM) have transformed the study of biomolecular structures by enabling the determination of complex structures at near-atomic res olution. However, many cryo-EM maps remain at resolutions that challenge accurate structural modeling. This project explores the application of deep learning techniques for enhancing the resolution of cryo-EM density maps, thereby improving their suitability for downstream protein structure modeling. By testing existing state-of-the-art models such as DeepEMhancer and EM-GAN, we aim to quantify improvements in structural interpretability and assess scenarios where these models underperform. Through this study, we propose to analyze and address the limitations of current models, eventually contributing to the development of more robust tools for resolution enhancement in structural biology. 2024-11-27T00:00:00Z http://repository.iiitd.edu.in/xmlui/handle/123456789/2054 Epiglobe: unleashing the power of knowledge graphs for enhanced data analysis and insights Singh, Ankit; Ahuja, Gaurav (Advisor) This work shows an overall method of using more than one biomedical knowledge graph to improve learning with graphs in the field of biomedical study. We combined various knowledge graphs with entities like proteins, genes, diseases, metabolites, phenotypes and chemicals. At first, we made the data uniform by standardizing it to increase its compatibility and consistency. After that, we set up a graph database using Neo4j. This made it easier to manage and get data from the system. We then assessed different Knowledge Graph Embedding (KGE) models and Graph Neural Network (GNN) structures to measure their performance in downstream tasks - mainly link prediction. By doing multiple tests, we showed how our method can effectively use the combined knowledge stored in biomedical knowledge graphs for better predictive modelling and finding new information in biomedical research fields. 2024-04-29T00:00:00Z http://repository.iiitd.edu.in/xmlui/handle/123456789/2034 Development of a haptic device for surgical training in mixed reality Dharmender; Parihar, Ritesh; Shankhwar, Kalpana (Advisor) This project focuses on designing a haptic device integrated into a Virtual reality (VR) environment to enhance surgical training. Combining force feedback, sensor integration, and 3D modeling, the device offers a realistic simulation of surgical procedures. Using Unity for simulation, Blender for 3D modeling, and precise sensors for feedback, the system aims to bridge the gap between theoretical learning and practical expertise. The innovation lies in its immersive, interactive design and its potential for reducing errors in surgical training. 2024-11-27T00:00:00Z