http://repository.iiitd.edu.in/xmlui/handle/123456789/1243 CB 2026-11-03T14:54:55Z http://repository.iiitd.edu.in/xmlui/handle/123456789/2174 Data-driven stewardship intelligence for antimicrobial resistance in India Kaur, Jasmine; Sethi, Tavpritesh (Advisor); Singh, Harpreet (Advisor) Antimicrobial resistance (AMR) is one of the most urgent public health threats of the present century. Antimicrobial stewardship and surveillance are integral pillars of the response to AMR. However, connecting these pillars requires interpretable evidence on resistance levels, temporal change and local context. This thesis develops analytical and translational approaches to turn routine surveillance data into evidence for stewardship prioritisation.The first objective is to characterise longitudinal and geographical patterns of AMR in India using multicentric IAMRSN data from bloodstream and urinary tract infections. Resistance trends varied across organisms, antibiotics and regions. Imipenem resistance increased by an estimated 0.22 percentage points per month in Escherichia coli bloodstream isolates and 0.37 percentage points per month in Pseudomonas aeruginosa urinary isolates. Bayesian network modelling identified conditional associations with state-level Sustainable Development Goal indicators, without establishing causality.Building on these patterns, the second objective moved beyond retrospective description towards examining temporal lead-lag relationships using cross-correlation. Cefotaxime resistance in E. coli bloodstream isolates showed a leading temporal association with imipenem and meropenem resistance at approximately 9–10 months. These findings identify candidate surveillance indicators whose robustness and prospective predictive performance require further evaluation. The third objective is to develop an interpretable trajectory-based scorecard AMROrbit for AMR monitoring. AMROrbit jointly captures resistance burden and temporal momentum by estimating both baseline resistance and directional change, enabling countries, hospitals, or pathogen–drug combinations to be classified into interpretable stewardship trajectories. Applied to 5,920 country– organism–antibiotic–specimen trajectories, we observed 17.7% were spiralling-out in comparison to only 13.4% as spiralling-in. AMROrbit has been implemented as a web-based tool to analyse 10 susceptibility testing data. It is currently being evaluated with clinical partners at AIIMS New Delhi and Max Hospital, with planned scale-up across multiple AIIMS in collaboration with CDAC. This objective extends AMR surveillance from trend estimation to prioritisation, allowing decision-makers to identify where resistance is not only high, but also worsening. The fourth objective defined a translational pathway through AMRSense for generating and triangulating contextual baseline evidence from community records, awareness assessments, prescriptions, antibiotic-use data and laboratory surveillance. Completed contributions included the conceptual architecture, application prototypes, awareness materials, a Delphi-validated KAP instrument, and development and pilot testing of the community data-collection workflow. KAP data collection is ongoing, while wider deployment and evaluation of effects on antibiotic-use practices and resistance remain future work. AMRaura is a proposed multidimensional integration framework whose development and prospective validation fall outside the completed work reported in this thesis. Together, these objectives address a central actionability gap in AMR surveillance. The thesis argues that a major barrier to effective AMR stewardship is not only the absence of data, but the limited conversion of existing and emerging data streams into dynamic, interpretable, and actionable intelligence. The thesis connects resistance trend analysis, candidate temporal indicators and trajectory-based prioritisation with a pathway for contextual surveillance, providing an empirical and methodological foundation for more informed stewardship. 2026-07-01T00:00:00Z 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/123456789/2057 Function prediction of protein cavities using 3D shape descriptors and machine learning Mittal, Pratham; Ray, Arjun (Advisor) Understanding protein function at the structural level requires accurate representation of their biologically active forms. However, structures deposited in the Protein Data Bank (PDB) often include only the asymmetric unit from crystallographic experiments, leading to discrepancies with the true oligomeric state. Such inconsistencies introduce significant challenges in down- stream analyses, particularly in cavity detection, where inter-subunit voids may be misidentified as functional binding sites. This work aims to address these challenges by constructing a curated dataset of protein struc- tures that accurately represent their functional oligomeric assemblies. PDB files were prepro- cessed to eliminate structural artifacts and to reconstruct biologically relevant assemblies. The cleaned structures were then analyzed using the CICLOP tool to identify cavity-lining residues, from which geometric and residue-level features were extracted. These features will serve as the foundation for machine learning models aimed at predicting protein function based on cavity characteristics. By integrating structural data correction with robust cavity analysis, this research establishes a reliable and interpretable pipeline for protein function prediction, with potential applications in structural bioinformatics and drug discovery. 2025-07-18T00:00:00Z