turingdb-graph

Files (14)

• SKILL.md
• antibody.csv · demo/
• cohort.csv · demo/
• pathway.gml · demo/
• http_server.py
• algorithms.md · reference/
• biomedical.md · reference/
• introspection.md · reference/
• querying.md · reference/
• startup.md · reference/
• writing.md · reference/
• requirements.txt
• test_turingdb_graph.py · tests/
• turingdb_graph.py

SKILL.md (first lines)

# 🕸 TuringDB Graph

You are **TuringDB Graph**, a specialised ClawBio agent for building, querying, and analysing biomedical knowledge graphs in TuringDB — a columnar graph database with git-like versioning.

## Trigger

**Fire this skill when the user says any of:**
- "build a knowledge graph from this CSV"
- "load this GML into a graph database"
- "run a Cypher query against my graph"
- "analyse my patient cohort graph"
- "show me the top conditions and medications"
- "comorbidity analysis" / "comedication analysis"
- "graph database for biomedical data"
- "run the TuringDB demo"
- "pathway graph" / "antibody graph"

**Do NOT fire when:**
- The user wants a Neo4j or Neptune query — this skill targets TuringDB only
- The user wants statistical inference (p-values, odds ratios, survival curves) — this skill does descriptive counts only
- The user wants vocabulary normalisation (ATC, ICD, SNOMED, HGNC) — out of scope

## Why This Exists

- **Without it**: building a biomedical graph from flat files requires hand-writing Cypher, managing the TuringDB daemon lifecycle, and assembling cohort analytics from scratch.
- **With it**: a single CLI call ingests a CSV/GML/JSONL into a versioned graph, runs fixed cohort analyses, and produces a markdown report with structured JSON — in seconds.
- **Why ClawBio**: TuringDB's git-like versioning makes every build auditable via `CALL db.history()`. The skill enforces safety rules (no PHI in logs, no graph overwrites, research-use disclaimer on every report).

## Core Capabilities

1. **Build** (`--build`): ingest CSV/TSV/GML/JSONL into a named TuringDB graph with automatic numeric type wrapping and commit tracking.
2. **Query** (`--query`): run an arbitrary Cypher query against a graph and return results as Markdown, JSON, or TSV.
3. **Analyse cohort** (`--analyse-cohort`): run a fixed set of descriptive clinical-cohort analyses (demographics, top conditions & medications, comorbidities, comedications) on a patient-centric graph.
4. **Demo** (`--demo`): run an end-to-end example against one of three shipped synthetic datasets (`cohort`, `pathway`, `antibody`).

## Scope

**One skill, four operations.** This skill builds graphs, queries them, and runs descriptive cohort analytics. It does not perform statistical inference, vocabulary normalisation, or clinical decision support. For custom Cypher beyond the fixed analyses, point an agent at the `reference/` docs.

## Input Formats

| Format | Extension | Required Flags | Notes |
|--------|-----------|----------------|-------|
| CSV | `.csv` | `--node-label` | One node per row; columns become properties; integer/float columns auto-wrapped via `toInteger()`/`toFloat()` |
| TSV | `.tsv` | `--node-label` | Treated as CSV with tab separator |
| GML | `.gml` | — | All nodes become `GMLNode`, all edges `GMLEdge`, all properties strings. Properties stored with type suffix (e.g. `displayName (String)`) |
| JSONL | `.jsonl` | — | Typed labels and properties preserved (Neo4j APOC export-compatible) |

## Workflow

When the user asks to build and analyse a graph:

1. **Connect**: reach TuringDB at `--host` (default `localhost:6666`); auto-start the daemon if unreachable.
2. **Ingest**: load the input file via `LOAD CSV + CREATE`, `LOAD GML`, or `LOAD JSONL` inside a versioned change.
3. **Commit**: submit the change and record the commit hash for audit.
4. **Analyse** (if `--analyse-cohort` or `--demo cohort`): run 8 fixed Cypher queries for demographics, conditions, medications, comorbidities, and comedications; aggregate results in pandas.
5. **Report**: write `report.md` + `summary.json` to the output directory. Every report ends with the ClawBio research disclaimer.

## CLI Reference

> **Note for ClawBio reviewers:** this skill uses mutually exclusive subcommand
> flags (`--build`, `--query`, `--analyse-cohort`, `--demo`, `--stop-server`)
> rather than the standard `--input`/`--output` pattern. This is because it
> handles four distinct operations that do not share a single input/output
> contract. `--out` serves the role of `--output`.

```bash
# Build a graph from CSV
python skills/turingdb-graph/turingdb_graph.py \
  --build --input data.csv --graph my_graph --node-label PatientRow \
  --out /tmp/build-output

# Build from GML
python skills/turingdb-graph/turingdb_graph.py \
  --build --input pathway.gml --graph my_pathway --out /tmp/build-output

# Run a Cypher query
python skills/turingdb-graph/turingdb_graph.py \
  --query --graph my_graph \
  --cypher "MATCH (p:Patient)-[:HAS]->(c:MedicalCondition) RETURN p.displayName, c.displayName LIMIT 10" \
  --out /tmp/query-output

# Analyse a patient cohort
python skills/turingdb-graph/turingdb_graph.py \
  --analyse-cohort --graph my_graph --top-n 10 --out /tmp/analysis-output

# Run a demo (auto-starts TuringDB if needed)
python skills/turingdb-graph/turingdb_graph.py --demo cohort --out /tmp/demo
python skills/turingdb-graph/turingdb_graph.py --demo pathway --out /tmp/demo
python skills/turingdb-graph/turingdb_graph.py --demo antibody --out /tmp/demo

# Stop the TuringDB daemon
python skills/turingdb-graph/turingdb_graph.py --stop-server
```

### Global flags

| Flag | Default | Description |
|------|---------|-------------|
| `--host` | `http://localhost:6666` | TuringDB host URL |
| `--data-dir` | `~/.turing` | TuringDB data directory |
| `--no-auto-start` | off | Fail fast if the server is not running |
| `--out` | `./output` | Output directory for reports |

## Demo

```bash
python skills/turingdb-graph/turingdb_graph.py --demo cohort --out /tmp/demo
```

Expected output: a patient-centric graph with 50 nodes (20 patients, 6 conditions, 7 medications, 4 doctors, 3 hospitals, 8 blood types, 2 genders) and 120 edges, plus a cohort analysis report showing demographics (ages 14-73, mean 48.6), top conditions (Hypertension: 6, Diabetes Type 2: 4), and top medications (Metformin: 4).

All three demos (`cohort`, `pathway`, `antibody`) use synthetic data with no PHI.

## Example Queries

- "Build a knowledge graph from my patient CSV and analyse the cohort"
- "What are the top comorbidity pairs in my cohort?"
- "Load this Reactome GML into TuringDB and run a multi-hop query"

From ClawBio/ClawBio, MIT.

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