http://repository.iiitd.edu.in/xmlui/handle/123456789/1250 2026-09-02T06:43:40Z 2026-09-02T06:43:40Z Mittal, Pratham Ray, Arjun (Advisor) http://repository.iiitd.edu.in/xmlui/handle/123456789/2057 2026-08-31T22:00:44Z 2025-07-18T00:00:00Z

Title: Function prediction of protein cavities using 3D shape descriptors and machine learning Authors: Mittal, Pratham; Ray, Arjun (Advisor) Abstract: 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 Goel, Rahul Mutharaju, Raghava (Advisor) http://repository.iiitd.edu.in/xmlui/handle/123456789/2056 2026-08-31T22:00:24Z 2025-07-18T00:00:00Z

Title: Embedding morality in AI systems Authors: Goel, Rahul; Mutharaju, Raghava (Advisor) Abstract: The increasing integration of Artificial Intelligence into complex societal domains necessitates a deeper understanding of how to embed ethical reasoning into machines. This project addresses the challenge of quantifying and predicting alignment with established human ethical frameworks. Our methodology involved three key stages. First, we established a granular theoretical framework by defining 15 distinct ethical theories under the three major schools of thought: Consequentialism (α), Deontology (β), and Virtue Ethics (γ). Second, we developed a novel dataset of 450 ethical scenarios, specifically designed to elicit responses corresponding to each of the 15 theories. These scenarios were then annotated using Large Language Models (LLMs) to generate quantitative scores (α, β, γ) representing the relevance of each ethical school to a given case. Finally, we benchmarked a suite of classical machine learning models to predict these ethical alignments from textual features. Two primary experiments were conducted: a multi-output regression task to predict the (α, β, γ) scores and a multi-output classification task to predict the specific ethical school and theory. In the regression task, Linear Regression demonstrated the strongest explanatory power, achieving an R-squared (R2 ) value of 0.5804. For classification, Logistic Regression was the top- performing model, achieving an Exact Match Accuracy of 74.44% and 100% accuracy in identifying the high-level ethical school. This work provides a foundational dataset, a robust evaluation of classical machine learning models for ethical prediction, and confirms that quantitative representations of ethical schools are viable targets for computational modeling. The findings lay the groundwork for future research in building more transparent, auditable, and ethically-aligned AI systems.

2025-07-18T00:00:00Z Singh, Ankit Ahuja, Gaurav (Advisor) http://repository.iiitd.edu.in/xmlui/handle/123456789/2054 2026-08-31T22:00:45Z 2024-04-29T00:00:00Z

Title: Epiglobe: unleashing the power of knowledge graphs for enhanced data analysis and insights Authors: Singh, Ankit; Ahuja, Gaurav (Advisor) Abstract: 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 Bhaskar, Siddharth Bali, Siddhant Kumar, Rishabh Ghosh, Tarini Shankar (Advisor) http://repository.iiitd.edu.in/xmlui/handle/123456789/2039 2026-08-28T22:00:45Z 2025-07-17T00:00:00Z

Title: FMT hub phase II: comprehensive web infrastructure for the FMT center and the microbiome informatics lab at IIIT-Delhi Authors: Bhaskar, Siddharth; Bali, Siddhant; Kumar, Rishabh; Ghosh, Tarini Shankar (Advisor) Abstract: We present FMT Hub - Phase II, We are developing the official website for the Microbiome Informatics Lab at IIIT Delhi as a digital extension of the FMT Hub. The platform is being built uscentralized 15, React 19, Node.js, Tailwind CSS, and TypeScript, focusing on modular UI, SEO, and responsiveness. We’re implementing secure APIs, dynamic routing, and scalable backend architecture for seamless performance. Deployment is handled on Oracle Linux with Apache, using SSL-secured HTTPS. This site will serve as a centralized digital gateway for lab research, data resources, and collaborations.

2025-07-17T00:00:00Z