Enterprise AI Analysis
Revolutionizing Immune Program Interpretation with Context-Aware AI
AI-powered Immune Cell Knowledge Graphs (ICKGs) transform immune cell profiling by integrating fragmented knowledge into context-aware, interpretable insights. Built from over 24,000 cancer immunotherapy PubMed abstracts using LLMs and validated with perturbation datasets, ICKGs capture directed relationships among genes, pathways, and immune functions, offering superior contextual understanding compared to traditional databases. This framework facilitates mechanistic hypothesis generation and accurate pathway annotations in single-cell and spatial omics.
Driving Innovation in Immunological Research
Our AI-powered ICKGs deliver unprecedented depth and accuracy, enabling breakthroughs in understanding immune cell function and therapeutic development.
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PubMed Abstracts Processed
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Cell Type-Specific ICKGs
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NER Precision
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Prediction Accuracy
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
ICKG Construction
Biological Reasoning Validation
Gene Set Annotation Performance
Discussion & Future Applications
ICKG Construction Methodology
The Immune Cell Knowledge Graphs (ICKGs) were systematically built by extracting and organizing information from over 24,000 cancer immunotherapy-focused PubMed abstracts. This process leveraged fine-tuned BioBERT models for Named Entity Recognition (NER) of genes, diseases, and cell types, achieving high precision (e.g., 87.87% for genes). Pathways were extracted using a rule-based approach combined with prompt engineering. Directional relationships (activation/inhibition) between entities were inferred using the Llama 3.1 model, with 'human verifiable' validation. This results in context-specific, literature-supported relationships, unlike conventional databases.
Validated Biological Reasoning
ICKGs demonstrate superior predictive power for immunological perturbations. Using PageRank on ICKGs consistently yielded significantly higher overlap with ground-truth differentially expressed genes (DEGs) compared to control experiments and canonical databases like Hallmark gene sets. This indicates that ICKGs capture more granular and functionally relevant immune contexts. The cell-type specificity of ICKGs is a dominant determinant of predictive accuracy, showing that lineage-specific wiring drives accurate inference across diverse immune settings.
Enhanced Gene Set Annotation
ICKG-based gene set annotation significantly outperforms conventional enrichment tools. Unlike static, broadly defined gene sets in canonical databases, ICKGs provide more concise, interpretable, and context-specific annotations. This approach achieved similar semantic relevance but with significantly higher specificity (smaller within-cluster sum of squares) compared to ORA and LLM-based methods. ICKGs successfully annotated gene sets where traditional enrichment tests failed, broadening their applicability to small or novel gene sets and providing literature-verified reasoning paths.
Forward-Looking Discussion & Applications
The ICKG framework offers a scalable and transparent approach for unifying fragmented immunological insights. It provides literature-supported reasoning for discovery, gene set interpretation, and hypothesis generation. Future extensions include increasing granularity to cell subtypes/states, integrating more diverse data sources, and algorithmic refinements for probabilistic relationships. ICKGs are vital for contextualizing findings from single-cell and spatial omics, supporting initiatives like the Immune Cell Atlas, and bridging the gap between AI inference and human interpretability.
115,701
Cell-Type Specific Edges Captured by ICKGs (vs. KEGG's 19,583)
Enterprise Process Flow
PubMed Abstracts Download
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Named Entity Recognition (NER)
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Pathway Extraction
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Relationship Extraction (RE)
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Network Assembly
| Feature | ICKG Approach | Canonical Databases (e.g., KEGG, Hallmarks) |
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| Annotation Performance |
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Case Study: IL-15 Perturbation in NK Cells
Challenge: Understanding how a cytokine like IL-15 impacts NK cell function and identifying downstream effectors with context-specific accuracy, surpassing generic pathway annotations.
Solution: Applying PageRank reasoning on the NK-specific ICKG following an IL-15 perturbation. The ICKG leveraged its rich immune context to prioritize functionally relevant genes and pathways, specifically those involved in NK cell-mediated cytotoxicity.
Results: The NK-specific ICKG recovered 30 of 442 DEGs (Jaccard=0.11), significantly outperforming Hallmark's 'MYC Targets' (14 DEGs, Jaccard=0.05) which lacked NK-specific context. ICKG accurately identified key cytotoxic effectors (IFNG, PRF1, NKG2D, CD8, GZMA) and predicted STAT activation, aligning with known IL-15 signaling mechanisms.
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