Julia
Seamless Integration With your Workflow
Experience how Julia combines omics data analysis with LLM-powered insights for precision oncology
PatientData.xlsx
| 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.
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
Get Started
Begin your Analysis with Julia
1. Patient's Data
Import RNAseq data and blood sample results through Julia's Excel interface
- Gene expression profiles from RNAseq
- Serum biomarkers (CA-125, HE4)
- Clinical history and outcomes
2. Multilayer Analysis
Julia processes your data through her three specialized AI layers
- Virtual tumor behavior modeling
- Multi-omics data integration
- Treatment response prediction
3. Review Results
Get comprehensive analysis and treatment recommendations
- Personalized molecular insights
- Ranked treatment options with AI confidence
- Evidence-based protocol recommendations
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.