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Custom Text Analysis
Paste or upload your own biomedical text and let Ignet extract gene interactions, build networks, and provide AI-powered interpretation.
Or upload: TXT, PDF, DOCX
Or enter PubMed IDs
How it works
Entity Extraction
SciMiner identifies genes (25,256 HUGO symbols), drugs (153K DrugBank terms), diseases (11,840 HDO terms), vaccines (3,454 VO terms), and interaction types (1,051 INO terms).
Network Construction
Build interaction networks from your text. Score interactions with BioBERT. Type interactions with INO ontology. Visualize in Cytoscape.js.
Context Expansion
For each gene pair found, Ignet queries its full PubMed-mined database to add supporting evidence and discover connections your text may have missed.
AI Interpretation
LLM-powered summarization, hypothesis generation, and pathway comparison. Ask questions about your network in natural language.
Use cases
- 1. Manuscript review -- Identify all gene interactions, cross-reference with existing literature
- 2. Grant writing -- Map proposed interaction networks, identify evidence gaps
- 3. Lab notebook analysis -- Structure biological entities and interactions from experimental notes