Turbulance DSL: Executable Scientific Method
The Paradigm Shift
Instead of scientists using Hegel as just a processing tool, they write their entire experimental methodology as Turbulance scripts, and Hegel executes the scientific method itself with genuine understanding of what each step means scientifically.
Traditional Approach
π Statistical Processing
π’ Data β Numbers β Results
β No semantic understanding
Turbulance Revolution
π§ Semantic Understanding
π¬ Hypothesis β Execution β Insight
β Genuine scientific reasoning
Four-File Semantic System
Main Script
Core experimental methodology with semantic operations
Consciousness Visualization
Real-time semantic understanding visualization
Dependencies
Intelligence module orchestration and data sources
Decision Logging
Metacognitive decision tracking and authenticity validation
Example: Diabetes Biomarker Discovery
hypothesis "Type 2 diabetes progression involves metabolic pathway dysregulation detectable through multi-omics integration"
# Semantic data integration with V8 intelligence
funxn load_patient_data():
proteomics_data = spectacular.load_ms_data("patients/*.mzML")
genomics_data = mzekezeke.load_variants("patients/*.vcf")
metabolomics_data = hatata.load_metabolites("patients/*.csv")
# Semantic integration, not just concatenation
return diggiden.integrate_modalities(proteomics_data, genomics_data, metabolomics_data)
# Proposition with semantic understanding
proposition diabetes_biomarkers = nicotine.discover_biomarkers(
patient_data,
phenotype="diabetes_progression",
semantic_context="metabolic_dysregulation"
)
# Motion: Execute with genuine understanding
motion validate_biomarkers:
for biomarker in diabetes_biomarkers:
# Semantic validation, not just statistical
authenticity = pungwe.validate_authenticity(biomarker)
biological_relevance = champagne.assess_relevance(biomarker, "diabetes")
if authenticity > 0.8 and biological_relevance > 0.7:
yield biomarkerV8 Intelligence Network
Mzekezeke
ML workhorse with semantic learning
Diggiden
Adversarial authenticity validation
Hatata
Decision processes with genuine understanding
Spectacular
Anomaly detection with semantic context
Nicotine
Biomarker discovery with biological insight
Pungwe
Cross-modal integration and validation
Zengeza
Dream processing for novel insights
Champagne
Biological relevance assessment
Getting Started with Turbulance
# Compile and execute Turbulance script
cargo run --bin hegel compile-turbulance --project diabetes_study/
# Execute with semantic understanding
cargo run --bin hegel execute-turbulance --script diabetes_study.trb
# API integration
curl -X POST "http://localhost:8080/turbulance/compile-and-execute" \
-H "Content-Type: application/json" \
-d '{"script": "hypothesis \"...\"\nfunxn load_data(): ..."}'The Problem with Binary Evidence
Traditional Approach
Forces continuous biological evidence into binary classifications, losing critical uncertainty information.
Hegel's Innovation
Preserves the continuous nature of biological evidence with fuzzy membership degrees and uncertainty quantification.
Federated Evidence Collaboration
The Data Access Challenge
Most valuable biological evidence is distributed across institutions and often inaccessible due to:
- Privacy regulations (HIPAA, GDPR)
- Competitive concerns in pharmaceutical research
- Institutional data governance policies
- Technical barriers to data sharing
Federated Learning Solution
Inspired by Bloodhound, Hegel enables:
- Local-First Processing: Data never leaves its source
- Pattern-Only Sharing: Only learned insights are shared
- Zero-Configuration: Automatic peer discovery and setup
- Privacy-Preserving: Differential privacy and secure aggregation
Federated Architecture
Institution A
Institution B
Institution C
Federated Aggregation
Patterns Only - No Raw Data
Hybrid Fuzzy-Bayesian Evidence System
Fuzzy Logic Framework
Continuous membership functions replace binary classifications
- Triangular, Gaussian, Trapezoidal, Sigmoid functions
- Linguistic variables: very_low β very_high
- T-norms and S-norms for evidence combination
Bayesian Networks
Probabilistic reasoning with fuzzy evidence integration
- Hybrid fuzzy-Bayesian inference
- Evidence relationship modeling
- Posterior probability calculation
Evidence Networks
Learn relationships and predict missing evidence
- Automatic relationship discovery
- Missing evidence prediction
- Network coherence optimization
Specialized Intelligence Modules
Mzekezeke - ML Workhorse
Primary predictive engine with ensemble methods
- Multi-modal biological data learning
- Continuous model adaptation
- Automated feature engineering
Diggiden - Adversarial System
Persistent vulnerability detection and testing
- Adversarial robustness testing
- Evidence consistency auditing
- Federated security monitoring
Hatata - Decision System
Markov decision processes with utility optimization
- Probabilistic state modeling
- Multi-objective optimization
- Adaptive policy learning
Spectacular - Anomaly Handler
Specialized processing for extraordinary findings
- Multi-method anomaly detection
- Novel pattern recognition
- Extraordinary event classification
Mathematical Foundation
ΞΌ(evidence): Fuzzy membership degree of evidence
P(evidence|identity): Likelihood weighted by fuzzy confidence
P(identity): Network-based priors from evidence relationships
System Architecture
Frontend Layer
API Layer
Intelligence Modules
Core Engine
Data Layer
Performance Advantages
Research Applications
Proteomics
Continuous confidence scoring for protein identifications with temporal decay modeling for aging spectral libraries.
- Multi-dimensional uncertainty analysis
- Complex sample processing
- Spectral library management
Metabolomics
Fuzzy structural similarity assessment with continuous membership in metabolic pathways.
- Uncertainty-aware biomarker discovery
- Pathway membership analysis
- Structural similarity scoring
Multi-omics Integration
Fuzzy evidence fusion across genomics, transcriptomics, and proteomics with coherent uncertainty propagation.
- Cross-platform evidence integration
- Missing data prediction
- Uncertainty propagation
Systems Biology
Evidence-based pathway reconstruction with network coherence optimization and uncertainty-aware modeling.
- Pathway reconstruction
- Network optimization
- Systems-level analysis
Precision Medicine
Patient-specific evidence networks with uncertainty-aware biomarker validation and personalized treatment pathways.
- Personalized medicine
- Biomarker validation
- Treatment optimization
Drug Discovery
Target identification with confidence bounds and evidence-based drug-target interaction prediction.
- Target identification
- Drug-target interactions
- Compound screening
Getting Started
Prerequisites
For core engine development
For backend development
For frontend development
For containerized deployment
Quick Start
# Clone the repository
git clone https://github.com/fullscreen-triangle/hegel.git
cd hegel
# Run setup script
chmod +x scripts/*.sh
./scripts/setup.sh
# Start development environment
./scripts/dev.sh
# Access the application
# Frontend: http://localhost:3000
# API Docs: http://localhost:8080/docs
# Neo4j Browser: http://localhost:7474Manual Development Setup
# Navigate to core directory
cd core
# Build the Rust core engine
cargo build --release
# Run fuzzy-Bayesian tests
cargo test
# Development with hot reloading
cargo watch -x check -x test# Navigate to backend directory
cd backend
# Create virtual environment
python -m venv venv
source venv/bin/activate
# Install dependencies
pip install -r requirements.txt
# Run the API
uvicorn app.main:app --reload# Navigate to frontend directory
cd frontend
# Install dependencies
yarn install
# Start development server
yarn dev