GraphRAG (Implementation Workflow) for extracting features from integrated multimodal (unstructured) Spatial Omics data and building knowledge graph, agents, and chatbot for targeted immunotherapy applications such as Immune Checkpoint Inhibitors (ICIs) in precision medicine.
GraphRAG (Graph-based Retrieval Augmented Generation)
GraphRAG is a powerful tool for extracting information from unstructured data and understanding complex interaction patterns, phenotypes. By leveraging knowledge graphs, GraphRAG can capture relationships and structures that are often missed by traditional vector search methods.
How GraphRAG Works:
1.Document Chunking: Unstructured documents are segmented into manageable chunks, preserving context and meaning.
2.Entity Extraction: Key entities and their relationships are identified and extracted from the text.
3.Graph Construction: These entities and relationships are organized into a hierarchical graph structure, linking related entities and forming meta-graphs.
Information Retrieval: The graph structure allows for efficient retrieval of relevant information, enabling the generation of contextually rich and accurate responses.
Harnessing GraphRAG using Multimodal Spatial Omics Data for Advanced Biological Insights and Immunotherapy Clinical Applications in GenAI
GraphRAG offers significant advantages over the baseline RAG by enhancing the retrieval process through its integrated knowledge graphs and multimodal data, leading to more accurate and contextually relevant responses. Additionally, leveraging semantic relationships and spatial contextual data allows GraphRAG to provide deeper insights, particularly in the complex domain of targeted immunotherapy and personalized genomic medicine.
GraphRAG integrates knowledge graphs and large language models (LLMs) with multimodal data, including spatial omics, single-cell data (scRNA seq), and unstructured data from PubMed, pdf files, patients’ data (tabular, EHR) to understand complex biological relationships. This process involves generating KG, extracting features using LLMs, representing via nodes (cells, genes, proteins) and detecting communities (node clustering) to identify semantic relationships and interactions among genes and cells. GraphRAG retrieves relevant information from the knowledge graph, which includes various cell types such as B cells, T cells, and macrophages. The system can respond to specific queries by leveraging the integrated knowledge graph and LLMs to provide targeted responses. This approach is particularly relevant for understanding the contextual spatial relationships and cellular communication in tumor microenvironment (TME) in targeted immunotherapy, specifically immune checkpoint inhibitors (ICI), understanding resistance or response and for identifying predictive biomarkers in clinical applications.
