Julia
Seamless Integration With your Workflow
Experience how Julia combines omics data analysis with LLM-powered insights for precision oncology
| Patient ID | Growth Rate | Response | Gene Expression | CA-125 | HE4 | Treatment | Confidence |
|---|---|---|---|---|---|---|---|
| P-001 | 1.8x | 90% inhibition | BRCA1↑, TP53↓ | 85 U/mL | 110 pmol/L | PARP Inhibitor | 92% |
| P-002 | 2.1x | 85% inhibition | BRCA2↓, TP53↑ | 120 U/mL | 140 pmol/L | Platinum-based | 88% |
I'm Julia
Ask me anything
Virtual simulation for P-001 shows 1.8x growth rate. RNAseq indicates high BRCA1 expression. AI predicts high PARP inhibitor sensitivity. Would you like to see the detailed predictions?
Yes, show me the predictions.
AI predicts 92% treatment success with PARP inhibitors. Analysis combines virtual response with patient biomarkers (CA-125: 85 U/mL, HE4: 110 pmol/L). BRCA1 expression pattern indicates high drug sensitivity.
How does this compare to standard protocols for ovarian cancer?
Standard NCCN guidelines recommend PARP inhibitors for BRCA-mutated ovarian cancer. What makes this prediction unique is the spheroid response data combined with gene expression profiles. This patient's virtual twin spheroid showed 90% growth inhibition with olaparib, matching the molecular profile. This is 22% more accurate than predictions based on biomarkers alone.
Are there any potential side effects we should monitor?
Based on the patient's gene expression profile, there's a moderate risk (24%) for fatigue and anemia with PARP inhibitor therapy. I recommend CBC monitoring every 2 weeks for the first 2 months. The spheroid model suggests dose reduction may be needed at week 6. Would you like me to prepare a personalized monitoring schedule?
Features
Advanced Cancer Analysis Platform
Harness the power of twin spheroids and multilayer AI agents for personalized cancer care
Agent
Agent
Agent
Monitoring
Markers
Planning
Julia's Agentic Network
Managed by Julia, back-end agents work together to predict patient's tumor behavior, process RNAseq data, predict potential recurrence status and recommend optimal therapies based on established protocols.
Excel Integration
Seamlessly analyze spheroid data and molecular profiles through familiar Excel interface
Agentic Spheroids
Intelligent agents model tumor behavior to predict treatment responses with high accuracy
Protocol Compliance
Treatment recommendations aligned with NCCN, ESMO, and ASCO guidelines
Precision Matching
Match patients with optimal therapeutics using comprehensive molecular profiles
Use Cases
Julia's Precision Oncology Solutions
Empowering oncologists with AI-driven insights for personalized patient care
Molecular Analysis
Process RNAseq data and blood sample markers (including CA-125 and HE4) to understand each patient's unique molecular profile
Response Prediction
Predict treatment responses using agentic spheroids and machine learning models trained on clinical data
Treatment Planning
Generate personalized treatment recommendations based on molecular profiles and protocol guidelines
FAQ
Common Questions
Julia uses a three-layer AI architecture: (1) Foundation Layer with agentic spheroids for tumor modeling, (2) Patient Data Layer processing RNAseq and blood sample data, and (3) Treatment Layer matching patients with optimal therapies based on standard oncology protocols.
Julia can process RNAseq data, blood sample markers (including CA-125 and HE4), and clinical data through an intuitive Excel interface. She also integrates with standard oncology protocols from NCCN, ESMO, and ASCO.
Julia provides confidence scores with each prediction, based on extensive training on clinical data and agentic spheroid modeling. Her multi-layer architecture ensures comprehensive analysis and validation against established medical protocols.