Year-2026 http://repository.iiitd.edu.in/xmlui/handle/123456789/1793 2026-11-03T14:54:43Z 2026-11-03T14:54:43Z Kaur, Jasmine Sethi, Tavpritesh (Advisor) Singh, Harpreet (Advisor) http://repository.iiitd.edu.in/xmlui/handle/123456789/2174 2026-10-31T22:00:48Z 2026-07-01T00:00:00Z

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 Choudhury, Shubham Raghava, Gajendra Pal Singh (Advisor) http://repository.iiitd.edu.in/xmlui/handle/123456789/2030 2026-08-25T22:00:35Z 2026-07-01T00:00:00Z

Development of AI-based tools for functional annotation of lncRNA and its applications Choudhury, Shubham; Raghava, Gajendra Pal Singh (Advisor) Long non-coding RNAs (lncRNAs) are a significant component of the human transcriptome and are well known for their ability to regulate gene expression. They have been implicated in a variety of disease-associated pathways. However, functional annotation of these lncRNAs remains a major challenge in the era of genomics and metagenomics, where large-scale genome sequencing has become routine. In this thesis, we have attempted to develop computational resources for annotating lncRNAs and to explore their applications in cancer diagnostics. In the first part of this thesis, we compiled all lncRNAs resources that includes experimental techniques, prediction methods and data repositories. These resources were manually curated from the literature and made available to the scientific community through a web platform called lncInfo. Next, we developed CytoLNCpred, a cell line–specific computational method for identifying cytoplasm-associated lncRNAs across 15 human cell lines. We implemented multiple computational approaches, including machine learning, deep learning, and large language models; in order to achieve improved prediction accuracy. The second part of the thesis describes a method, lncrnaPI, developed to predict lncRNA–protein interacting pairs using transformer-based sequence embeddings as features. In the final section of the thesis, we explored the cancer diagnostic potential of lncRNAs based on their transcriptomic profiles. Specifically, we analyzed lncRNA transcriptomic profiles in patients with pancreatic ductal adenocarcinoma (PDAC). We explored two independent approaches for identifying lncRNA-based biomarkers capable of discriminating PDAC patients from healthy individuals. First, we developed machine learning–based models using traditional approaches for biomarker identification. Second, we investigated a new concept in which large language models were used for mining transcriptomic data. One key objective of this research work is to facilitate the scientific community working in the field of lncRNA biology. Therefore, we developed web-based servers and standalone tools that are publicly available fostering open science. The work presented in this thesis provides novel computational strategies for enhancing the functional annotation of lncRNAs, decoding their molecular interactions, and exploiting their expression patterns for clinically meaningful applications.

2026-07-01T00:00:00Z Kumar, Nishant Raghava, Gajendra Pal Singh (Advisor) http://repository.iiitd.edu.in/xmlui/handle/123456789/2029 2026-08-25T22:00:44Z 2026-03-01T00:00:00Z

Development of AI-based tools for designing subunit vaccines Kumar, Nishant; Raghava, Gajendra Pal Singh (Advisor) Immunization continues to be a cornerstone of global public health, drastically reducing mortality rates worldwide. Over time, the field of vaccinology has progressively shifted from utilizing whole-pathogen preparations toward highly targeted epitope and subunit formulations. However, traditional laboratory-based methods for vaccine discovery remain heavily constrained by high financial costs and extended developmental timelines. To mitigate these bottlenecks, this thesis introduces a comprehensive suite of computational resources for designing epitope or subunit based vaccines. The work presented in this thesis is organized into three major components: identification of epitope-based vaccine candidates, safety evaluation of predicted candidates, and assessment of stability under physiological and environmental conditions. Under the first objective, a tool named CLBtope was developed to predict both linear and conformational B-cell epitopes from antigen sequences, facilitating the identification of potential vaccine candidates capable of eliciting protective B-cell and antibody responses. To ensure candidate safety, two predictive models were established: DMPPred, which identifies peptides associated with type 1 diabetes mellitus, and AlgPred3, which comprehensively evaluates the allergenic risks of proposed vaccine components. Finally, to address peptide and protein stability under diverse physiological and environmental conditions, three distinct platforms were developed: AFProPred for the characterization and design of antifreeze proteins, PPTStab for engineering thermostable proteins, and PlifePred2 for estimating peptide half-lives in body fluids. All tools developed in this thesis are freely available to the scientific community as web servers and standalone software through platforms such as GitHub and PyPI. State-of-the-art AI methodologies, including machine learning, deep learning, and large language models, were deployed to design effective subunit vaccines.

2026-03-01T00:00:00Z Singh, Pradeep Sethi, Tavpritesh (Advisor) Lodha, Rakesh (Advisor) http://repository.iiitd.edu.in/xmlui/handle/123456789/1995 2026-09-10T22:00:43Z 2026-05-01T00:00:00Z

SAFE-ICU: advancing AI-driven decision support in the intensive care units Singh, Pradeep; Sethi, Tavpritesh (Advisor); Lodha, Rakesh (Advisor) Integrating artificial intelligence into healthcare presents an unprecedented opportunity to address critical challenges in Intensive Care Units. The objective of this thesis is to create, validate, and deploy AI models to increase safety through the development of early warning systems for drug-drug interactions and predicting clinical decompensation of patients. Our first objective is creation of a data lake involving multimodal data, which combines vital signs, laboratory tests, treatment histories, patient files, and thermographic videos; not just images. The combination of structured and unstructured data contained in the datasets offers unmatched insight into intensive care unit patients, thereby enhancing the overall quality of research. The datasets used for prediction and diagnostics are pre-processed through various techniques like feature extraction, temporal alignment, etc. The second Objective aims at the creation of actionable, predictive, and management tools for sophisticated critical care of ICU patients. For example, ThermalShockNet, which was created for pediatric ICU settings and utilizes thermal videos and patient vitals to identify subtle physiological changes indicative of hemodynamic shock. This model uses data from thermal frames and vital signs to make predictions. When combined using deep learning, these predictions can reveal the onset of shock before it appears in a patient. ThermoGnose, a pipeline developed specifically for the early prediction of hypothermia, uses non-linear features drawn from patient monitoring data to estimate hypothermia onset and provide vital lead time for clinical intervention. SIgnose predicts shock risk by analyzing vital sign trends. When combined, this variety of approaches highlight how AI can address complex challenges in the ICU. The third Objective was to improve drug safety in the ICU. For this, the ContraIndicator tool was created. NLP processes the treatment chart and line listing of drugs to find probable adverse drug–drug interactions. The ContraIndicator insights assist clinicians in minimizing the chance of adverse reactions. The end Objective was SafeICU, a modular and explainable AI-driven real-time platform for the pediatric ICU. It brings together early warning systems along with safety and infection surveillance modules, which include vital signs, thermal imaging, medication, and microbiology results. At SafeICU, they help flag unusual Shock Index values in many ICUs. The ContraIndicator is on the lookout for drug interactions through the examination of ICU prescriptions. AMRtrack monitors the emergence of antimicrobial resistance by processing laboratory reports, mapping trend data, and informing infection control planning. When these modules are together, real-time alerts, risk scores, and infection monitoring are delivered into the ICU workflow. Overall, this thesis provides a clinically oriented framework for using AI in ICUs. It shows how models that can be multimodal, explainable, and deployable work together in real time to assist clinical decision making in high-acuity clinical settings. These advances create a pathway to the secure and scalable exploitation of AI in critical care and, eventually, precision medicine in health care that are rich in data.

2026-05-01T00:00:00Z