{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Notice**: The `semantica.kg_qa` module is temporarily unavailable and will be reintroduced in a future release. Any quality assessment examples in this notebook are disabled."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/Hawksight-AI/semantica/blob/main/cookbook/use_cases/biomedical/02_Genomic_Variant_Analysis.ipynb)\n",
    "\n",
    "# Genomic Variant Analysis Pipeline\n",
    "\n",
    "## Overview\n",
    "\n",
    "This notebook demonstrates a complete genomic variant analysis pipeline: ingest genomic data from multiple sources (APIs, databases, feeds), extract variant entities, build genomic knowledge graph, analyze disease associations, predict variant impact, and perform pathway analysis.\n",
    "\n",
    "\n",
    "**Documentation**: [API Reference](https://semantica.readthedocs.io/use-cases/)\n",
    "\n",
    "### Modules Used (20+)\n",
    "\n",
    "- **Ingestion**: FileIngestor, WebIngestor, FeedIngestor, StreamIngestor, DBIngestor, RepoIngestor, EmailIngestor, MCPIngestor\n",
    "- **Parsing**: JSONParser, StructuredDataParser, DocumentParser\n",
    "- **Extraction**: NERExtractor, RelationExtractor, TripleExtractor, SemanticAnalyzer\n",
    "- **KG**: GraphBuilder, GraphAnalyzer, CentralityCalculator, CommunityDetector\n",
    "- **Analytics**: ConnectivityAnalyzer, TemporalGraphQuery, TemporalPatternDetector\n",
    "- **Ontology**: OntologyGenerator, ClassInferrer, PropertyGenerator, OntologyValidator\n",
    "- **Reasoning**: InferenceEngine, RuleManager, ExplanationGenerator\n",
    "- **Quality**: KGQualityAssessor, ConflictDetector\n",
    "- **Export**: JSONExporter, RDFExporter, OWLExporter, ReportGenerator\n",
    "- **Visualization**: KGVisualizer, OntologyVisualizer, AnalyticsVisualizer\n",
    "\n",
    "### Pipeline\n",
    "\n",
    "**Genomic Data Sources → Parse → Extract Entities (variants, genes, diseases, pathways) → Build Genomic KG → Analyze Associations → Predict Impact → Pathway Analysis → Generate Reports → Visualize**\n",
    "\n",
    "## Installation\n",
    "\n",
    "Install Semantica from PyPI:\n",
    "\n",
    "```bash\n",
    "pip install semantica\n",
    "# Or with all optional dependencies:\n",
    "pip install semantica[all]\n",
    "```\n",
    "\n",
    "---\n",
    "\n",
    "## Step 1: Ingest Genomic Data from Multiple Sources\n",
    "\n",
    "Ingest genomic variant data from APIs, databases, and research feeds.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from semantica.ingest import WebIngestor, DBIngestor, FeedIngestor, FileIngestor\n",
    "from semantica.parse import JSONParser, StructuredDataParser, DocumentParser\n",
    "from semantica.semantic_extract import NERExtractor, RelationExtractor, TripleExtractor, SemanticAnalyzer\n",
    "from semantica.kg import GraphBuilder, GraphAnalyzer, CentralityCalculator, CommunityDetector\n",
    "from semantica.kg import ConnectivityAnalyzer, TemporalGraphQuery, TemporalPatternDetector\n",
    "from semantica.ontology import OntologyGenerator, ClassInferrer, PropertyGenerator, OntologyValidator\n",
    "from semantica.reasoning import InferenceEngine, RuleManager, ExplanationGenerator\n",
    
    "from semantica.conflicts import ConflictDetector\n",
    "from semantica.export import JSONExporter, RDFExporter, OWLExporter, ReportGenerator\n",
    "from semantica.visualization import KGVisualizer, OntologyVisualizer, AnalyticsVisualizer\n",
    "import tempfile\n",
    "import os\n",
    "import json\n",
    "from datetime import datetime, timedelta\n",
    "\n",
    "web_ingestor = WebIngestor()\n",
    "db_ingestor = DBIngestor()\n",
    "feed_ingestor = FeedIngestor()\n",
    "file_ingestor = FileIngestor()\n",
    "\n",
    "json_parser = JSONParser()\n",
    "structured_parser = StructuredDataParser()\n",
    "document_parser = DocumentParser()\n",
    "\n",
    "# Real genomic APIs\n",
    "genomic_apis = [\n",
    "    \"https://rest.ensembl.org/variation/human/rs699\",  # Ensembl API\n",
    "    \"https://api.ncbi.nlm.nih.gov/variation/v0/variant/NC_000001.10:g.230710048A%3EG\",  # NCBI Variation API\n",
    "    \"https://api.ncbi.nlm.nih.gov/variation/v0/beta/refsnp/699\"  # ClinVar API\n",
    "]\n",
    "\n",
    "# Real genomic databases\n",
    "genomic_databases = [\n",
    "    \"dbSNP\",\n",
    "    \"ClinVar\",\n",
    "    \"COSMIC\"\n",
    "]\n",
    "\n",
    "# Real research feeds\n",
    "genomic_feeds = [\n",
    "    \"https://pubmed.ncbi.nlm.nih.gov/rss/search?term=genomic+variants\",\n",
    "    \"https://pubmed.ncbi.nlm.nih.gov/rss/search?term=genetic+variation\"\n",
    "]\n",
    "\n",
    "# Real database connection for variant annotations\n",
    "db_connection_string = \"postgresql://user:password@localhost:5432/genomic_db\"\n",
    "db_query = \"SELECT variant_id, gene_symbol, disease_name, clinical_significance, chromosome, position FROM variants WHERE clinical_significance IN ('Pathogenic', 'Likely Pathogenic') LIMIT 1000\"\n",
    "\n",
    "temp_dir = tempfile.mkdtemp()\n",
    "\n",
    "# Sample genomic variant data for local ingestion\n",
    "genomic_data_file = os.path.join(temp_dir, \"genomic_variants.json\")\n",
    "genomic_data = [\n",
    "    {\n",
    "        \"variant_id\": \"rs699\",\n",
    "        \"gene_symbol\": \"AGT\",\n",
    "        \"gene_name\": \"Angiotensinogen\",\n",
    "        \"disease_name\": \"Hypertension\",\n",
    "        \"clinical_significance\": \"Pathogenic\",\n",
    "        \"chromosome\": \"1\",\n",
    "        \"position\": 230710048,\n",
    "        \"ref_allele\": \"A\",\n",
    "        \"alt_allele\": \"G\",\n",
    "        \"pathway\": \"Renin-angiotensin system\",\n",
    "        \"impact\": \"High\",\n",
    "        \"timestamp\": (datetime.now() - timedelta(days=1)).isoformat()\n",
    "    },\n",
    "    {\n",
    "        \"variant_id\": \"rs7412\",\n",
    "        \"gene_symbol\": \"APOE\",\n",
    "        \"gene_name\": \"Apolipoprotein E\",\n",
    "        \"disease_name\": \"Alzheimer's Disease\",\n",
    "        \"clinical_significance\": \"Pathogenic\",\n",
    "        \"chromosome\": \"19\",\n",
    "        \"position\": 44908822,\n",
    "        \"ref_allele\": \"C\",\n",
    "        \"alt_allele\": \"T\",\n",
    "        \"pathway\": \"Lipid metabolism\",\n",
    "        \"impact\": \"High\",\n",
    "        \"timestamp\": (datetime.now() - timedelta(days=2)).isoformat()\n",
    "    },\n",
    "    {\n",
    "        \"variant_id\": \"rs1800566\",\n",
    "        \"gene_symbol\": \"NAT2\",\n",
    "        \"gene_name\": \"N-acetyltransferase 2\",\n",
    "        \"disease_name\": \"Drug Metabolism\",\n",
    "        \"clinical_significance\": \"Likely Pathogenic\",\n",
    "        \"chromosome\": \"8\",\n",
    "        \"position\": 18248728,\n",
    "        \"ref_allele\": \"G\",\n",
    "        \"alt_allele\": \"A\",\n",
    "        \"pathway\": \"Drug metabolism\",\n",
    "        \"impact\": \"Moderate\",\n",
    "        \"timestamp\": (datetime.now() - timedelta(days=3)).isoformat()\n",
    "    },\n",
    "    {\n",
    "        \"variant_id\": \"rs1799853\",\n",
    "        \"gene_symbol\": \"CYP2C9\",\n",
    "        \"gene_name\": \"Cytochrome P450 2C9\",\n",
    "        \"disease_name\": \"Warfarin Sensitivity\",\n",
    "        \"clinical_significance\": \"Pathogenic\",\n",
    "        \"chromosome\": \"10\",\n",
    "        \"position\": 96741054,\n",
    "        \"ref_allele\": \"C\",\n",
    "        \"alt_allele\": \"T\",\n",
    "        \"pathway\": \"Drug metabolism\",\n",
    "        \"impact\": \"High\",\n",
    "        \"timestamp\": (datetime.now() - timedelta(days=4)).isoformat()\n",
    "    },\n",
    "    {\n",
    "        \"variant_id\": \"rs1057910\",\n",
    "        \"gene_symbol\": \"CYP2C9\",\n",
    "        \"gene_name\": \"Cytochrome P450 2C9\",\n",
    "        \"disease_name\": \"Warfarin Sensitivity\",\n",
    "        \"clinical_significance\": \"Pathogenic\",\n",
    "        \"chromosome\": \"10\",\n",
    "        \"position\": 96741055,\n",
    "        \"ref_allele\": \"A\",\n",
    "        \"alt_allele\": \"C\",\n",
    "        \"pathway\": \"Drug metabolism\",\n",
    "        \"impact\": \"High\",\n",
    "        \"timestamp\": (datetime.now() - timedelta(days=5)).isoformat()\n",
    "    }\n",
    "]\n",
    "\n",
    "with open(genomic_data_file, 'w') as f:\n",
    "    json.dump(genomic_data, f, indent=2)\n",
    "\n",
    "# Ingest from local file\n",
    "file_data = file_ingestor.ingest_file(genomic_data_file)\n",
    "parsed_genomic = structured_parser.parse_json(json.dumps(genomic_data))\n",
    "\n",
    "# Ingest from genomic APIs (example with public API)\n",
    "web_content = web_ingestor.ingest_url(genomic_apis[0])  # Ensembl API\n",
    "if web_content:\n",
    "    print(f\"  Ingested genomic API: {genomic_apis[0]}\")\n",
    "\n",
    "# Ingest from genomic feeds\n",
    "feed_data_list = []\n",
    "for feed_url in genomic_feeds:\n",
    "    feed_data = feed_ingestor.ingest_feed(feed_url)\n",
    "    if feed_data:\n",
    "        feed_data_list.append(feed_data)\n",
    "        print(f\"  Ingested feed: {feed_url}\")\n",
    "\n",
    "# Database ingestion pattern\n",
    "db_data = db_ingestor.export_table(\n",
    "    connection_string=db_connection_string,\n",
    "    table_name=\"variants\",\n",
    "    limit=1000\n",
    ")\n",
    "print(f\"  Query pattern: {db_query}\")\n",
    "\n",
    "print(f\"\\n📊 Ingestion Summary:\")\n",
    "print(f\"  Local variants: {len(genomic_data)}\")\n",
    "print(f\"  Database records: {len(db_data.get('data', [])) if db_data else 0}\")\n",
    "print(f\"  Feeds ingested: {len(feed_data_list)}\")\n",
    "print(f\"  Web APIs: {len(genomic_apis)}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 2: Extract Genomic Entities\n",
    "\n",
    "Extract variants, genes, diseases, and pathways from the ingested data.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ner_extractor = NERExtractor()\n",
    "relation_extractor = RelationExtractor()\n",
    "triple_extractor = TripleExtractor()\n",
    "semantic_analyzer = SemanticAnalyzer()\n",
    "\n",
    "all_genomic_texts = []\n",
    "all_variants = []\n",
    "\n",
    "# Process parsed genomic data\n",
    "if parsed_genomic and isinstance(parsed_genomic, dict):\n",
    "    variants = parsed_genomic.get(\"data\", genomic_data)\n",
    "    for variant in variants:\n",
    "        all_variants.append(variant)\n",
    "        variant_text = f\"Variant {variant.get('variant_id', '')} in gene {variant.get('gene_symbol', '')} associated with {variant.get('disease_name', '')} in pathway {variant.get('pathway', '')}\"\n",
    "        all_genomic_texts.append(variant_text)\n",
    "\n",
    "# Extract entities\n",
    "all_entities = []\n",
    "all_relationships = []\n",
    "all_triples = []\n",
    "\n",
    "for text in all_genomic_texts:\n",
    "    entities = ner_extractor.extract(text)\n",
    "    all_entities.extend(entities)\n",
    "    \n",
    "    relationships = relation_extractor.extract(text, entities)\n",
    "    all_relationships.extend(relationships)\n",
    "    \n",
    "    triples = triple_extractor.extract(text)\n",
    "    all_triples.extend(triples)\n",
    "\n",
    "# Build structured entity list\n",
    "variant_entities = []\n",
    "gene_entities = []\n",
    "disease_entities = []\n",
    "pathway_entities = []\n",
    "\n",
    "unique_genes = {}\n",
    "unique_diseases = {}\n",
    "unique_pathways = {}\n",
    "\n",
    "for variant in all_variants:\n",
    "    variant_entity = {\n",
    "        \"id\": variant.get(\"variant_id\", \"\"),\n",
    "        \"type\": \"Variant\",\n",
    "        \"properties\": {\n",
    "            \"variant_id\": variant.get(\"variant_id\", \"\"),\n",
    "            \"chromosome\": variant.get(\"chromosome\", \"\"),\n",
    "            \"position\": variant.get(\"position\", 0),\n",
    "            \"ref_allele\": variant.get(\"ref_allele\", \"\"),\n",
    "            \"alt_allele\": variant.get(\"alt_allele\", \"\"),\n",
    "            \"clinical_significance\": variant.get(\"clinical_significance\", \"\"),\n",
    "            \"impact\": variant.get(\"impact\", \"\"),\n",
    "            \"timestamp\": variant.get(\"timestamp\", \"\")\n",
    "        }\n",
    "    }\n",
    "    variant_entities.append(variant_entity)\n",
    "    \n",
    "    # Add gene entity\n",
    "    gene_symbol = variant.get(\"gene_symbol\", \"\")\n",
    "    if gene_symbol and gene_symbol not in unique_genes:\n",
    "        gene_entity = {\n",
    "            \"id\": gene_symbol,\n",
    "            \"type\": \"Gene\",\n",
    "            \"properties\": {\n",
    "                \"symbol\": gene_symbol,\n",
    "                \"name\": variant.get(\"gene_name\", \"\"),\n",
    "                \"chromosome\": variant.get(\"chromosome\", \"\")\n",
    "            }\n",
    "        }\n",
    "        gene_entities.append(gene_entity)\n",
    "        unique_genes[gene_symbol] = gene_entity\n",
    "    \n",
    "    # Add disease entity\n",
    "    disease_name = variant.get(\"disease_name\", \"\")\n",
    "    if disease_name and disease_name not in unique_diseases:\n",
    "        disease_entity = {\n",
    "            \"id\": disease_name.replace(\" \", \"_\"),\n",
    "            \"type\": \"Disease\",\n",
    "            \"properties\": {\n",
    "                \"name\": disease_name\n",
    "            }\n",
    "        }\n",
    "        disease_entities.append(disease_entity)\n",
    "        unique_diseases[disease_name] = disease_entity\n",
    "    \n",
    "    # Add pathway entity\n",
    "    pathway_name = variant.get(\"pathway\", \"\")\n",
    "    if pathway_name and pathway_name not in unique_pathways:\n",
    "        pathway_entity = {\n",
    "            \"id\": pathway_name.replace(\" \", \"_\"),\n",
    "            \"type\": \"Pathway\",\n",
    "            \"properties\": {\n",
    "                \"name\": pathway_name\n",
    "            }\n",
    "        }\n",
    "        pathway_entities.append(pathway_entity)\n",
    "        unique_pathways[pathway_name] = pathway_entity\n",
    "\n",
    "print(f\"Extracted {len(variant_entities)} variants\")\n",
    "print(f\"Extracted {len(gene_entities)} unique genes\")\n",
    "print(f\"Extracted {len(disease_entities)} unique diseases\")\n",
    "print(f\"Extracted {len(pathway_entities)} unique pathways\")\n",
    "print(f\"Extracted {len(all_relationships)} relationships\")\n",
    "print(f\"Extracted {len(all_triples)} triples\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 3: Build Genomic Knowledge Graph\n",
    "\n",
    "Build a knowledge graph from extracted genomic entities and relationships.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "builder = GraphBuilder()\n",
    "\n",
    "# Add all entities\n",
    "for variant in variant_entities:\n",
    "    builder.add_entity(\n",
    "        entity_id=variant[\"id\"],\n",
    "        entity_type=variant[\"type\"],\n",
    "        properties=variant.get(\"properties\", {})\n",
    "    )\n",
    "\n",
    "for gene in gene_entities:\n",
    "    builder.add_entity(\n",
    "        entity_id=gene[\"id\"],\n",
    "        entity_type=gene[\"type\"],\n",
    "        properties=gene.get(\"properties\", {})\n",
    "    )\n",
    "\n",
    "for disease in disease_entities:\n",
    "    builder.add_entity(\n",
    "        entity_id=disease[\"id\"],\n",
    "        entity_type=disease[\"type\"],\n",
    "        properties=disease.get(\"properties\", {})\n",
    "    )\n",
    "\n",
    "for pathway in pathway_entities:\n",
    "    builder.add_entity(\n",
    "        entity_id=pathway[\"id\"],\n",
    "        entity_type=pathway[\"type\"],\n",
    "        properties=pathway.get(\"properties\", {})\n",
    "    )\n",
    "\n",
    "# Add relationships\n",
    "relationships = []\n",
    "for variant in variant_entities:\n",
    "    variant_id = variant[\"id\"]\n",
    "    gene_symbol = variant[\"properties\"].get(\"gene_symbol\", \"\")\n",
    "    disease_name = variant[\"properties\"].get(\"disease_name\", \"\").replace(\" \", \"_\")\n",
    "    pathway_name = variant[\"properties\"].get(\"pathway\", \"\").replace(\" \", \"_\")\n",
    "    \n",
    "    # Find corresponding entities\n",
    "    gene_entity = unique_genes.get(gene_symbol)\n",
    "    disease_entity = unique_diseases.get(disease_name.replace(\"_\", \" \"))\n",
    "    pathway_entity = unique_pathways.get(pathway_name.replace(\"_\", \" \"))\n",
    "    \n",
    "    if gene_entity:\n",
    "        builder.add_relationship(\n",
    "            source_id=variant_id,\n",
    "            target_id=gene_entity[\"id\"],\n",
    "            relationship_type=\"located_in\",\n",
    "            properties={}\n",
    "        )\n",
    "        relationships.append({\n",
    "            \"source\": variant_id,\n",
    "            \"target\": gene_entity[\"id\"],\n",
    "            \"type\": \"located_in\"\n",
    "        })\n",
    "    \n",
    "    if disease_entity:\n",
    "        builder.add_relationship(\n",
    "            source_id=variant_id,\n",
    "            target_id=disease_entity[\"id\"],\n",
    "            relationship_type=\"associated_with\",\n",
    "            properties={\n",
    "                \"clinical_significance\": variant[\"properties\"].get(\"clinical_significance\", \"\")\n",
    "            }\n",
    "        )\n",
    "        relationships.append({\n",
    "            \"source\": variant_id,\n",
    "            \"target\": disease_entity[\"id\"],\n",
    "            \"type\": \"associated_with\"\n",
    "        })\n",
    "    \n",
    "    if pathway_entity:\n",
    "        builder.add_relationship(\n",
    "            source_id=gene_entity[\"id\"] if gene_entity else variant_id,\n",
    "            target_id=pathway_entity[\"id\"],\n",
    "            relationship_type=\"participates_in\",\n",
    "            properties={}\n",
    "        )\n",
    "        relationships.append({\n",
    "            \"source\": gene_entity[\"id\"] if gene_entity else variant_id,\n",
    "            \"target\": pathway_entity[\"id\"],\n",
    "            \"type\": \"participates_in\"\n",
    "        })\n",
    "\n",
    "knowledge_graph = builder.build()\n",
    "\n",
    "print(f\"Built knowledge graph with {len(knowledge_graph.nodes)} nodes\")\n",
    "print(f\"Built knowledge graph with {len(knowledge_graph.edges)} edges\")\n",
    "print(f\"Added {len(relationships)} genomic relationships\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 4: Analyze Associations and Predict Impact\n",
    "\n",
    "Analyze variant-disease associations and predict variant impact on protein function.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "graph_analyzer = GraphAnalyzer()\n",
    "centrality_calculator = CentralityCalculator()\n",
    "community_detector = CommunityDetector()\n",
    "connectivity_analyzer = ConnectivityAnalyzer()\n",
    "temporal_query = TemporalGraphQuery(knowledge_graph)\n",
    "pattern_detector = TemporalPatternDetector()\n",
    "\n",
    "# Compute graph metrics\n",
    "graph_metrics = graph_analyzer.compute_metrics(knowledge_graph)\n",
    "\n",
    "# Calculate centrality\n",
    "centrality_result = centrality_calculator.calculate_betweenness_centrality(knowledge_graph)\n",
    "centrality_scores = centrality_result.get('centrality', {})\n",
    "top_central_genes = sorted(centrality_scores.items(), key=lambda x: x[1], reverse=True)[:10]\n",
    "\n",
    "# Detect communities\n",
    "communities_result = community_detector.detect_communities(knowledge_graph, algorithm=\"louvain\")\n",
    "communities = communities_result.get('communities', [])\n",
    "community_count = len(communities) if communities else 0\n",
    "\n",
    "# Analyze connectivity\n",
    "connectivity_results = connectivity_analyzer.analyze_connectivity(knowledge_graph)\n",
    "\n",
    "# Detect temporal patterns\n",
    "start_time = (datetime.now() - timedelta(days=7)).isoformat()\n",
    "end_time = datetime.now().isoformat()\n",
    "temporal_results = temporal_query.query_time_range(\n",
    "    start_time=start_time,\n",
    "    end_time=end_time,\n",
    "    relationship_types=[\"associated_with\", \"located_in\"]\n",
    ")\n",
    "\n",
    "temporal_patterns = pattern_detector.detect_temporal_patterns(\n",
    "    knowledge_graph,\n",
    "    relationship_types=[\"associated_with\"],\n",
    "    time_window_hours=168\n",
    ")\n",
    "\n",
    "# Impact Prediction using Inference Engine\n",
    "inference_engine = InferenceEngine()\n",
    "rule_manager = RuleManager()\n",
    "\n",
    "# Define impact prediction rules\n",
    "impact_rules = [\n",
    "    {\n",
    "        \"name\": \"high_impact_pathogenic\",\n",
    "        \"condition\": \"clinical_significance == 'Pathogenic' AND impact == 'High'\",\n",
    "        \"action\": \"predict_high_impact\"\n",
    "    },\n",
    "    {\n",
    "        \"name\": \"moderate_impact_likely_pathogenic\",\n",
    "        \"condition\": \"clinical_significance == 'Likely Pathogenic' AND impact == 'Moderate'\",\n",
    "        \"action\": \"predict_moderate_impact\"\n",
    "    },\n",
    "    {\n",
    "        \"name\": \"disease_association\",\n",
    "        \"condition\": \"associated_with_disease AND pathogenic\",\n",
    "        \"action\": \"predict_disease_risk\"\n",
    "    }\n",
    "]\n",
    "\n",
    "for rule in impact_rules:\n",
    "    rule_manager.add_rule(rule[\"name\"], rule[\"condition\"], rule[\"action\"])\n",
    "\n",
    "# Predict variant impact\n",
    "variant_impacts = []\n",
    "for variant in variant_entities:\n",
    "    clinical_sig = variant[\"properties\"].get(\"clinical_significance\", \"\")\n",
    "    impact = variant[\"properties\"].get(\"impact\", \"\")\n",
    "    \n",
    "    impact_score = 0\n",
    "    if clinical_sig == \"Pathogenic\":\n",
    "        impact_score += 5\n",
    "    elif clinical_sig == \"Likely Pathogenic\":\n",
    "        impact_score += 3\n",
    "    \n",
    "    if impact == \"High\":\n",
    "        impact_score += 3\n",
    "    elif impact == \"Moderate\":\n",
    "        impact_score += 2\n",
    "    \n",
    "    # Check disease associations\n",
    "    disease_associations = [r for r in relationships if r[\"source\"] == variant[\"id\"] and r[\"type\"] == \"associated_with\"]\n",
    "    if disease_associations:\n",
    "        impact_score += 2\n",
    "    \n",
    "    variant_impacts.append({\n",
    "        \"variant\": variant[\"id\"],\n",
    "        \"impact_score\": min(impact_score, 10),\n",
    "        \"predicted_impact\": \"High\" if impact_score >= 7 else \"Moderate\" if impact_score >= 4 else \"Low\",\n",
    "        \"clinical_significance\": clinical_sig,\n",
    "        \"disease_associations\": len(disease_associations)\n",
    "    })\n",
    "\n",
    "print(f\"Analyzed {len(variant_entities)} variants\")\n",
    "print(f\"Found {community_count} gene communities\")\n",
    "print(f\"Detected {len(temporal_patterns)} temporal patterns\")\n",
    "print(f\"\\nTop 5 Central Genes:\")\n",
    "for i, (gene_id, centrality) in enumerate(top_central_genes[:5], 1):\n",
    "    print(f\"  {i}. {gene_id} (centrality: {centrality:.3f})\")\n",
    "print(f\"\\nVariant Impact Predictions:\")\n",
    "for impact in sorted(variant_impacts, key=lambda x: x[\"impact_score\"], reverse=True):\n",
    "    print(f\"  - {impact['variant']}: {impact['predicted_impact']} Impact (Score: {impact['impact_score']}/10, Diseases: {impact['disease_associations']})\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 5: Generate Genomic Ontology and Pathway Analysis\n",
    "\n",
    "Generate genomic ontology and perform pathway analysis.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ontology_generator = OntologyGenerator()\n",
    "class_inferrer = ClassInferrer()\n",
    "property_generator = PropertyGenerator()\n",
    "ontology_validator = OntologyValidator()\n",
    "\n",
    "# Generate genomic ontology\n",
    "genomic_ontology = ontology_generator.generate_ontology(\n",
    "    {\"entities\": variant_entities + gene_entities + disease_entities + pathway_entities, \n",
    "     \"relationships\": relationships},\n",
    "    name=\"GenomicsOntology\",\n",
    "    entities=variant_entities + gene_entities + disease_entities + pathway_entities,\n",
    "    relationships=relationships\n",
    ")\n",
    "\n",
    "# Infer classes/properties (for inspection)\n",
    "classes = class_inferrer.infer_classes(variant_entities + gene_entities + disease_entities + pathway_entities)\n",
    "properties = property_generator.infer_properties(variant_entities + gene_entities + disease_entities + pathway_entities, relationships, classes)\n",
    "\n",
    "# Ensure ontology dict has classes/properties\n",
    "if not genomic_ontology.get(\"classes\"):\n",
    "    genomic_ontology[\"classes\"] = classes\n",
    "if not genomic_ontology.get(\"properties\"):\n",
    "    genomic_ontology[\"properties\"] = properties\n",
    "\n",
    "# Validate ontology\n",
    "validation_result = ontology_validator.validate_ontology(genomic_ontology)\n",
    "\n",
    "# Pathway analysis\n",
    "pathway_analysis = {}\n",
    "for pathway in pathway_entities:\n",
    "    pathway_id = pathway[\"id\"]\n",
    "    pathway_name = pathway[\"properties\"].get(\"name\", \"\")\n",
    "    \n",
    "    # Find variants and genes in this pathway\n",
    "    pathway_variants = [r for r in relationships if r[\"target\"] == pathway_id and r[\"type\"] == \"participates_in\"]\n",
    "    pathway_genes = set()\n",
    "    for rel in pathway_variants:\n",
    "        source_entity = next((e for e in variant_entities + gene_entities if e[\"id\"] == rel[\"source\"]), None)\n",
    "        if source_entity and source_entity[\"type\"] == \"Gene\":\n",
    "            pathway_genes.add(source_entity[\"id\"])\n",
    "    \n",
    "    pathway_analysis[pathway_name] = {\n",
    "        \"variants\": len([r for r in pathway_variants if next((e for e in variant_entities if e[\"id\"] == r[\"source\"]), None)]),\n",
    "        \"genes\": len(pathway_genes),\n",
    "        \"diseases\": len([r for r in relationships if r[\"source\"] in [e[\"id\"] for e in variant_entities] and r[\"type\"] == \"associated_with\"])\n",
    "    }\n",
    "\n",
    "print(f\"Generated genomic ontology with {len(genomic_ontology.get('classes', []))} classes\")\n",
    "print(f\"Ontology validation: {'Valid' if validation_result.valid else 'Invalid'}\")\n",
    "print(f\"  Errors: {len(validation_result.errors)}\")\n",
    "print(f\"  Warnings: {len(validation_result.warnings)}\")\n",
    "print(f\"\\nPathway Analysis:\")\n",
    "for pathway_name, analysis in pathway_analysis.items():\n",
    "    print(f\"  - {pathway_name}: {analysis['variants']} variants, {analysis['genes']} genes, {analysis['diseases']} disease associations\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 6: Generate Reports and Visualize\n",
    "\n",
    "Generate comprehensive genomic analysis reports and visualizations.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "json_exporter = JSONExporter()\n",
    "rdf_exporter = RDFExporter()\n",
    "owl_exporter = OWLExporter()\n",
    "report_generator = ReportGenerator()\n",
    "kg_quality_assessor = KGQualityAssessor()\n",
    "conflict_detector = ConflictDetector()\n",
    "\n",
    "# Assess graph quality\n",
    "quality_metrics = kg_quality_assessor.assess_quality(knowledge_graph)\n",
    "\n",
    "# Detect conflicts\n",
    "conflicts = conflict_detector.detect_conflicts(knowledge_graph)\n",
    "\n",
    "# Export knowledge graph\n",
    "kg_json = json_exporter.export(knowledge_graph, output_path=os.path.join(temp_dir, \"genomic_kg.json\"))\n",
    "kg_rdf = rdf_exporter.export(knowledge_graph, output_path=os.path.join(temp_dir, \"genomic_kg.rdf\"))\n",
    "\n",
    "# Export ontology\n",
    "ontology_owl = owl_exporter.export(genomic_ontology, output_path=os.path.join(temp_dir, \"genomic_ontology.owl\"))\n",
    "\n",
    "# Generate report\n",
    "report_content = f\"\"\"\n",
    "# Genomic Variant Analysis Report\n",
    "\n",
    "## Executive Summary\n",
    "- Total Variants Analyzed: {len(variant_entities)}\n",
    "- Unique Genes: {len(gene_entities)}\n",
    "- Unique Diseases: {len(disease_entities)}\n",
    "- Unique Pathways: {len(pathway_entities)}\n",
    "- High Impact Variants: {len([v for v in variant_impacts if v['predicted_impact'] == 'High'])}\n",
    "\n",
    "## Graph Quality Metrics\n",
    "- Nodes: {quality_metrics.get('node_count', len(knowledge_graph.nodes))}\n",
    "- Edges: {quality_metrics.get('edge_count', len(knowledge_graph.edges))}\n",
    "- Completeness: {quality_metrics.get('completeness', 0):.2%}\n",
    "- Consistency: {quality_metrics.get('consistency', 0):.2%}\n",
    "\n",
    "## Top Variants by Impact\n",
    "\"\"\"\n",
    "for i, impact in enumerate(sorted(variant_impacts, key=lambda x: x[\"impact_score\"], reverse=True)[:10], 1):\n",
    "    report_content += f\"\"\"\n",
    "{i}. {impact['variant']}\n",
    "   - Predicted Impact: {impact['predicted_impact']}\n",
    "   - Impact Score: {impact['impact_score']}/10\n",
    "   - Clinical Significance: {impact['clinical_significance']}\n",
    "   - Disease Associations: {impact['disease_associations']}\n",
    "\"\"\"\n",
    "\n",
    "report_content += f\"\"\"\n",
    "## Pathway Analysis\n",
    "\"\"\"\n",
    "for pathway_name, analysis in pathway_analysis.items():\n",
    "    report_content += f\"\"\"\n",
    "### {pathway_name}\n",
    "- Variants: {analysis['variants']}\n",
    "- Genes: {analysis['genes']}\n",
    "- Disease Associations: {analysis['diseases']}\n",
    "\"\"\"\n",
    "\n",
    "report_path = os.path.join(temp_dir, \"genomic_analysis_report.md\")\n",
    "with open(report_path, 'w') as f:\n",
    "    f.write(report_content)\n",
    "\n",
    "print(f\"Exported knowledge graph to JSON and RDF\")\n",
    "print(f\"Exported ontology to OWL\")\n",
    "print(f\"Generated analysis report: {report_path}\")\n",
    "print(f\"\\nQuality Metrics:\")\n",
    "print(f\"  Nodes: {quality_metrics.get('node_count', len(knowledge_graph.nodes))}\")\n",
    "print(f\"  Edges: {quality_metrics.get('edge_count', len(knowledge_graph.edges))}\")\n",
    "print(f\"  Completeness: {quality_metrics.get('completeness', 0):.2%}\")\n",
    "print(f\"  Consistency: {quality_metrics.get('consistency', 0):.2%}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 7: Visualize Genomic Network\n",
    "\n",
    "Visualize the genomic knowledge graph, ontology, and analytics.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "kg_visualizer = KGVisualizer()\n",
    "ontology_visualizer = OntologyVisualizer()\n",
    "analytics_visualizer = AnalyticsVisualizer()\n",
    "\n",
    "# Visualize knowledge graph\n",
    "kg_viz = kg_visualizer.visualize(\n",
    "    knowledge_graph,\n",
    "    layout=\"force_directed\",\n",
    "    highlight_nodes=[v[\"id\"] for v in variant_entities],\n",
    "    node_size_by=\"impact\"\n",
    ")\n",
    "\n",
    "# Visualize ontology\n",
    "ontology_viz = ontology_visualizer.visualize(\n",
    "    genomic_ontology,\n",
    "    layout=\"hierarchical\"\n",
    ")\n",
    "\n",
    "# Visualize analytics\n",
    "analytics_viz = analytics_visualizer.visualize(\n",
    "    knowledge_graph,\n",
    "    metrics={\n",
    "        \"centrality\": dict(top_central_genes[:10]),\n",
    "        \"communities\": communities,\n",
    "        \"connectivity\": connectivity_results,\n",
    "        \"impact_scores\": {v[\"variant\"]: v[\"impact_score\"] for v in variant_impacts}\n",
    "    }\n",
    ")\n"
   ]
  }
 ],
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