Implementation of advanced GNN methods (MOGONET, MOGAT, MPK-GNN) that integrate biological prior knowledge into graph neural network architectures for improved multi-omics data analysis.
Key Technologies: Multi-modal Graph Learning, Prior Knowledge Integration, Omics Data Fusion Status: Methodology development and benchmarking
Development and application of advanced GNN architectures (GCN, GAT, CompGCN) for biological data analysis. Focus on node classification, link prediction, and graph-level tasks using standard benchmarks (Cora, Cornell, Chameleon) and biological datasets.
Key Technologies: PyTorch Geometric, Deep Graph Library (DGL), NetworkX, Graph Neural Networks Status: Ongoing research at Cambridge University
Implementation of LLM-driven knowledge graph construction and retrieval-augmented generation systems for biological data. This project combines large language models with structured biological knowledge to enhance information retrieval and reasoning.
Key Technologies: Large Language Models, Knowledge Graph Embeddings, RAG Architecture, Neo4j Status: Active development with applications to nutrigenetics and general biological datasets
Development of comprehensive knowledge graphs spanning multiple species for drug repurposing applications. Using GCN and CompGCN architectures for link prediction and drug-target interaction discovery.
Key Technologies: Knowledge Graph Embeddings, Cross-species Data Integration, Drug Repurposing Algorithms Status: In preparation for publication
Advanced computational methods for single-cell RNA sequencing data analysis using graph-based approaches. Integration with tools like Seurat and development of novel network-based methods.
Key Technologies: scRNA-seq Analysis, Seurat, Graph-based Clustering, Cell Type Classification Status: Active research collaboration
Programming Languages
Graph Learning Frameworks
AI/ML Frameworks
Bioinformatics Tools
Data Management
Visiting Researcher - Department of Computer Science, University of Cambridge
Contribution to the development of a Large Language Model-powered chatbot for RNASeq expression data analysis, making transcriptomic analysis more accessible to researchers.
Technologies: LLMs, Transcriptomics, RNA-seq Analysis Status: Delivered and operational
Development of Graph-based Retrieval-Augmented Generation systems applied to biological datasets, with potential for journal publication and scalability to larger datasets.
Technologies: GraphRAG, LLMs, Knowledge Graph Construction Status: Presented at workshop, under development for journal submission
Implementation of knowledge graph construction spanning multiple species with Graph Convolutional Networks (GCN) and Composition-based GCN (CompGCN) for drug repurposing applications.
Technologies: Cross-species KG, GCN, CompGCN, Link Prediction Status: In preparation for publication