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queries/queries_bioinformatics.jsonl

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+{"id": "bioinformatics_T1_328", "domain": "bioinformatics", "type": "T1_entity", "query": "What is ARACNE?", "ground_truth": ["ARACNE", "TRNS"], "concept_id": 328, "hop_depth": 0}
+{"id": "bioinformatics_T1_58", "domain": "bioinformatics", "type": "T1_entity", "query": "What is GenBank Format?", "ground_truth": ["GenBank Format", "DFMT"], "concept_id": 58, "hop_depth": 0}
+{"id": "bioinformatics_T1_13", "domain": "bioinformatics", "type": "T1_entity", "query": "What is Translation?", "ground_truth": ["Translation", "FOUND"], "concept_id": 13, "hop_depth": 0}
+{"id": "bioinformatics_T1_380", "domain": "bioinformatics", "type": "T1_entity", "query": "What is Drug Repurposing?", "ground_truth": ["Drug Repurposing", "PATH"], "concept_id": 380, "hop_depth": 0}
+{"id": "bioinformatics_T1_141", "domain": "bioinformatics", "type": "T1_entity", "query": "What is Distributed Graph Databases?", "ground_truth": ["Distributed Graph Databases", "GRDB"], "concept_id": 141, "hop_depth": 0}
+{"id": "bioinformatics_T1_126", "domain": "bioinformatics", "type": "T1_entity", "query": "What is Aggregation in Cypher?", "ground_truth": ["Aggregation in Cypher", "GRDB"], "concept_id": 126, "hop_depth": 0}
+{"id": "bioinformatics_T1_115", "domain": "bioinformatics", "type": "T1_entity", "query": "What is Memgraph?", "ground_truth": ["Memgraph", "GRDB"], "concept_id": 115, "hop_depth": 0}
+{"id": "bioinformatics_T1_72", "domain": "bioinformatics", "type": "T1_entity", "query": "What is Nodes and Edges?", "ground_truth": ["Nodes and Edges", "GRTH"], "concept_id": 72, "hop_depth": 0}
+{"id": "bioinformatics_T1_378", "domain": "bioinformatics", "type": "T1_entity", "query": "What is Drug Target?", "ground_truth": ["Drug Target", "PATH"], "concept_id": 378, "hop_depth": 0}
+{"id": "bioinformatics_T1_53", "domain": "bioinformatics", "type": "T1_entity", "query": "What is REST APIs for Biology?", "ground_truth": ["REST APIs for Biology", "DBAS"], "concept_id": 53, "hop_depth": 0}
+{"id": "bioinformatics_T1_347", "domain": "bioinformatics", "type": "T1_entity", "query": "What is KEGG Pathways?", "ground_truth": ["KEGG Pathways", "PATH"], "concept_id": 347, "hop_depth": 0}
+{"id": "bioinformatics_T1_457", "domain": "bioinformatics", "type": "T1_entity", "query": "What is Seaborn?", "ground_truth": ["Seaborn", "TOOL"], "concept_id": 457, "hop_depth": 0}
+{"id": "bioinformatics_T1_280", "domain": "bioinformatics", "type": "T1_entity", "query": "What is Network Centrality in PPIs?", "ground_truth": ["Network Centrality in PPIs", "PPIS"], "concept_id": 280, "hop_depth": 0}
+{"id": "bioinformatics_T1_45", "domain": "bioinformatics", "type": "T1_entity", "query": "What is Gene Ontology Database?", "ground_truth": ["Gene Ontology Database", "DBAS"], "concept_id": 45, "hop_depth": 0}
+{"id": "bioinformatics_T1_303", "domain": "bioinformatics", "type": "T1_entity", "query": "What is Coverage?", "ground_truth": ["Coverage", "GENO"], "concept_id": 303, "hop_depth": 0}
+{"id": "bioinformatics_T1_217", "domain": "bioinformatics", "type": "T1_entity", "query": "What is Secondary Structure?", "ground_truth": ["Secondary Structure", "STRU"], "concept_id": 217, "hop_depth": 0}
+{"id": "bioinformatics_T1_17", "domain": "bioinformatics", "type": "T1_entity", "query": "What is Cell Biology Basics?", "ground_truth": ["Cell Biology Basics", "FOUND"], "concept_id": 17, "hop_depth": 0}
+{"id": "bioinformatics_T1_16", "domain": "bioinformatics", "type": "T1_entity", "query": "What is Molecular Biology?", "ground_truth": ["Molecular Biology", "FOUND"], "concept_id": 16, "hop_depth": 0}
+{"id": "bioinformatics_T1_48", "domain": "bioinformatics", "type": "T1_entity", "query": "What is BioCyc Database?", "ground_truth": ["BioCyc Database", "DBAS"], "concept_id": 48, "hop_depth": 0}
+{"id": "bioinformatics_T1_112", "domain": "bioinformatics", "type": "T1_entity", "query": "What is Relational Database?", "ground_truth": ["Relational Database", "GRDB"], "concept_id": 112, "hop_depth": 0}
+{"id": "bioinformatics_T1_120", "domain": "bioinformatics", "type": "T1_entity", "query": "What is RETURN Clause?", "ground_truth": ["RETURN Clause", "GRDB"], "concept_id": 120, "hop_depth": 0}
+{"id": "bioinformatics_T1_259", "domain": "bioinformatics", "type": "T1_entity", "query": "What is Network Hubs?", "ground_truth": ["Network Hubs", "PPIS"], "concept_id": 259, "hop_depth": 0}
+{"id": "bioinformatics_T1_309", "domain": "bioinformatics", "type": "T1_entity", "query": "What is Phasing?", "ground_truth": ["Phasing", "GENO"], "concept_id": 309, "hop_depth": 0}
+{"id": "bioinformatics_T1_14", "domain": "bioinformatics", "type": "T1_entity", "query": "What is Gene Expression?", "ground_truth": ["Gene Expression", "FOUND"], "concept_id": 14, "hop_depth": 0}
+{"id": "bioinformatics_T1_288", "domain": "bioinformatics", "type": "T1_entity", "query": "What is Assembly Quality Metrics?", "ground_truth": ["Assembly Quality Metrics", "GENO"], "concept_id": 288, "hop_depth": 0}
+{"id": "bioinformatics_T1_102", "domain": "bioinformatics", "type": "T1_entity", "query": "What is Small-World Networks?", "ground_truth": ["Small-World Networks", "GRTH"], "concept_id": 102, "hop_depth": 0}
+{"id": "bioinformatics_T1_367", "domain": "bioinformatics", "type": "T1_entity", "query": "What is Signal Transduction?", "ground_truth": ["Signal Transduction", "PATH"], "concept_id": 367, "hop_depth": 0}
+{"id": "bioinformatics_T1_333", "domain": "bioinformatics", "type": "T1_entity", "query": "What is Bayesian Network Model?", "ground_truth": ["Bayesian Network Model", "TRNS"], "concept_id": 333, "hop_depth": 0}
+{"id": "bioinformatics_T1_360", "domain": "bioinformatics", "type": "T1_entity", "query": "What is Synthetic Biology?", "ground_truth": ["Synthetic Biology", "PATH"], "concept_id": 360, "hop_depth": 0}
+{"id": "bioinformatics_T1_215", "domain": "bioinformatics", "type": "T1_entity", "query": "What is Comparative Genomics?", "ground_truth": ["Comparative Genomics", "PHYL"], "concept_id": 215, "hop_depth": 0}
+{"id": "bioinformatics_T1_113", "domain": "bioinformatics", "type": "T1_entity", "query": "What is Graph vs Relational Model?", "ground_truth": ["Graph vs Relational Model", "GRDB"], "concept_id": 113, "hop_depth": 0}
+{"id": "bioinformatics_T1_230", "domain": "bioinformatics", "type": "T1_entity", "query": "What is Contact Map as Graph?", "ground_truth": ["Contact Map as Graph", "STRU"], "concept_id": 230, "hop_depth": 0}
+{"id": "bioinformatics_T1_302", "domain": "bioinformatics", "type": "T1_entity", "query": "What is Sequencing Depth?", "ground_truth": ["Sequencing Depth", "GENO"], "concept_id": 302, "hop_depth": 0}
+{"id": "bioinformatics_T1_143", "domain": "bioinformatics", "type": "T1_entity", "query": "What is Graph Scalability?", "ground_truth": ["Graph Scalability", "GRDB"], "concept_id": 143, "hop_depth": 0}
+{"id": "bioinformatics_T1_415", "domain": "bioinformatics", "type": "T1_entity", "query": "What is Knowledge Graph Embedding?", "ground_truth": ["Knowledge Graph Embedding", "KNOW"], "concept_id": 415, "hop_depth": 0}
+{"id": "bioinformatics_T1_446", "domain": "bioinformatics", "type": "T1_entity", "query": "What is Clinical Data Graph?", "ground_truth": ["Clinical Data Graph", "KNOW"], "concept_id": 446, "hop_depth": 0}
+{"id": "bioinformatics_T1_4", "domain": "bioinformatics", "type": "T1_entity", "query": "What is DNA Structure?", "ground_truth": ["DNA Structure", "FOUND"], "concept_id": 4, "hop_depth": 0}
+{"id": "bioinformatics_T1_389", "domain": "bioinformatics", "type": "T1_entity", "query": "What is Biomarker Discovery?", "ground_truth": ["Biomarker Discovery", "PATH"], "concept_id": 389, "hop_depth": 0}
+{"id": "bioinformatics_T1_413", "domain": "bioinformatics", "type": "T1_entity", "query": "What is Node2Vec?", "ground_truth": ["Node2Vec", "KNOW"], "concept_id": 413, "hop_depth": 0}
+{"id": "bioinformatics_T1_82", "domain": "bioinformatics", "type": "T1_entity", "query": "What is Degree Distribution?", "ground_truth": ["Degree Distribution", "GRTH"], "concept_id": 82, "hop_depth": 0}
+{"id": "bioinformatics_T1_358", "domain": "bioinformatics", "type": "T1_entity", "query": "What is Minimal Growth Medium?", "ground_truth": ["Minimal Growth Medium", "PATH"], "concept_id": 358, "hop_depth": 0}
+{"id": "bioinformatics_T1_175", "domain": "bioinformatics", "type": "T1_entity", "query": "What is Sequence Profile?", "ground_truth": ["Sequence Profile", "SEQA"], "concept_id": 175, "hop_depth": 0}
+{"id": "bioinformatics_T1_80", "domain": "bioinformatics", "type": "T1_entity", "query": "What is Subgraph?", "ground_truth": ["Subgraph", "GRTH"], "concept_id": 80, "hop_depth": 0}
+{"id": "bioinformatics_T1_111", "domain": "bioinformatics", "type": "T1_entity", "query": "What is Graph Database?", "ground_truth": ["Graph Database", "GRDB"], "concept_id": 111, "hop_depth": 0}
+{"id": "bioinformatics_T1_391", "domain": "bioinformatics", "type": "T1_entity", "query": "What is Side Effect Prediction?", "ground_truth": ["Side Effect Prediction", "PATH"], "concept_id": 391, "hop_depth": 0}
+{"id": "bioinformatics_T1_173", "domain": "bioinformatics", "type": "T1_entity", "query": "What is Progressive Alignment?", "ground_truth": ["Progressive Alignment", "SEQA"], "concept_id": 173, "hop_depth": 0}
+{"id": "bioinformatics_T1_195", "domain": "bioinformatics", "type": "T1_entity", "query": "What is Trees as DAGs?", "ground_truth": ["Trees as DAGs", "PHYL"], "concept_id": 195, "hop_depth": 0}
+{"id": "bioinformatics_T1_50", "domain": "bioinformatics", "type": "T1_entity", "query": "What is Hetionet Database?", "ground_truth": ["Hetionet Database", "DBAS"], "concept_id": 50, "hop_depth": 0}
+{"id": "bioinformatics_T1_184", "domain": "bioinformatics", "type": "T1_entity", "query": "What is Distance Matrix?", "ground_truth": ["Distance Matrix", "PHYL"], "concept_id": 184, "hop_depth": 0}
+{"id": "bioinformatics_T1_434", "domain": "bioinformatics", "type": "T1_entity", "query": "What is Louvain Algorithm?", "ground_truth": ["Louvain Algorithm", "GRTH"], "concept_id": 434, "hop_depth": 0}
+{"id": "bioinformatics_T2_183", "domain": "bioinformatics", "type": "T2_dependency", "query": "What are the prerequisites for Molecular Phylogenetics?", "ground_truth": ["Phylogenetics", "Sequence Homology"], "concept_id": 183, "hop_depth": 1}
+{"id": "bioinformatics_T2_316", "domain": "bioinformatics", "type": "T2_dependency", "query": "What are the prerequisites for Differential Expression?", "ground_truth": ["Transcript Quantification"], "concept_id": 316, "hop_depth": 1}
+{"id": "bioinformatics_T2_139", "domain": "bioinformatics", "type": "T2_dependency", "query": "What are the prerequisites for Graph Query Optimization?", "ground_truth": ["Cypher Query Language", "Query Profiling"], "concept_id": 139, "hop_depth": 1}
+{"id": "bioinformatics_T2_421", "domain": "bioinformatics", "type": "T2_dependency", "query": "What are the prerequisites for Graph Neural Networks?", "ground_truth": ["Graph Embeddings", "Nodes and Edges"], "concept_id": 421, "hop_depth": 1}
+{"id": "bioinformatics_T2_24", "domain": "bioinformatics", "type": "T2_dependency", "query": "What are the prerequisites for Insertion and Deletion?", "ground_truth": ["Mutations"], "concept_id": 24, "hop_depth": 1}
+{"id": "bioinformatics_T2_381", "domain": "bioinformatics", "type": "T2_dependency", "query": "What are the prerequisites for Drug-Target-Disease Graph?", "ground_truth": ["Drug Target", "Directed Graphs"], "concept_id": 381, "hop_depth": 1}
+{"id": "bioinformatics_T2_242", "domain": "bioinformatics", "type": "T2_dependency", "query": "What are the prerequisites for Ligand-Protein Interaction?", "ground_truth": ["Protein Structure"], "concept_id": 242, "hop_depth": 1}
+{"id": "bioinformatics_T2_281", "domain": "bioinformatics", "type": "T2_dependency", "query": "What are the prerequisites for Genome Assembly?", "ground_truth": ["Genome", "Sequence Data"], "concept_id": 281, "hop_depth": 1}
+{"id": "bioinformatics_T2_65", "domain": "bioinformatics", "type": "T2_dependency", "query": "What are the prerequisites for SBML Format?", "ground_truth": ["FASTA Format"], "concept_id": 65, "hop_depth": 1}
+{"id": "bioinformatics_T2_200", "domain": "bioinformatics", "type": "T2_dependency", "query": "What are the prerequisites for Incomplete Lineage Sorting?", "ground_truth": ["Gene Tree vs Species Tree"], "concept_id": 200, "hop_depth": 1}
+{"id": "bioinformatics_T2_42", "domain": "bioinformatics", "type": "T2_dependency", "query": "What are the prerequisites for STRING Database?", "ground_truth": ["Biological Databases"], "concept_id": 42, "hop_depth": 1}
+{"id": "bioinformatics_T2_289", "domain": "bioinformatics", "type": "T2_dependency", "query": "What are the prerequisites for Reference Genome?", "ground_truth": ["Genome Assembly"], "concept_id": 289, "hop_depth": 1}
+{"id": "bioinformatics_T2_155", "domain": "bioinformatics", "type": "T2_dependency", "query": "What are the prerequisites for PAM Matrix?", "ground_truth": ["Scoring Matrices"], "concept_id": 155, "hop_depth": 1}
+{"id": "bioinformatics_T2_432", "domain": "bioinformatics", "type": "T2_dependency", "query": "What are the prerequisites for Graph Model for Multi-Omics?", "ground_truth": ["Unified Omics Graph", "Graph Schema Design"], "concept_id": 432, "hop_depth": 1}
+{"id": "bioinformatics_T2_328", "domain": "bioinformatics", "type": "T2_dependency", "query": "What are the prerequisites for ARACNE?", "ground_truth": ["Gene Regulatory Network", "Mutual Information"], "concept_id": 328, "hop_depth": 1}
+{"id": "bioinformatics_T2_323", "domain": "bioinformatics", "type": "T2_dependency", "query": "What are the prerequisites for Cis-Regulatory Element?", "ground_truth": ["Promoter Region", "Enhancer Region"], "concept_id": 323, "hop_depth": 1}
+{"id": "bioinformatics_T2_463", "domain": "bioinformatics", "type": "T2_dependency", "query": "What are the prerequisites for Workflow Managers?", "ground_truth": ["Reproducible Analysis"], "concept_id": 463, "hop_depth": 1}
+{"id": "bioinformatics_T2_449", "domain": "bioinformatics", "type": "T2_dependency", "query": "What are the prerequisites for Network-Based Biomarkers?", "ground_truth": ["Patient Stratification", "Biomarker Discovery"], "concept_id": 449, "hop_depth": 1}
+{"id": "bioinformatics_T2_192", "domain": "bioinformatics", "type": "T2_dependency", "query": "What are the prerequisites for Branch Support Values?", "ground_truth": ["Bootstrap Analysis"], "concept_id": 192, "hop_depth": 1}
+{"id": "bioinformatics_T2_302", "domain": "bioinformatics", "type": "T2_dependency", "query": "What are the prerequisites for Sequencing Depth?", "ground_truth": ["Next-Gen Sequencing"], "concept_id": 302, "hop_depth": 1}
+{"id": "bioinformatics_T2_101", "domain": "bioinformatics", "type": "T2_dependency", "query": "What are the prerequisites for Scale-Free Networks?", "ground_truth": ["Degree Distribution", "Power-Law Distribution"], "concept_id": 101, "hop_depth": 1}
+{"id": "bioinformatics_T2_368", "domain": "bioinformatics", "type": "T2_dependency", "query": "What are the prerequisites for Receptor?", "ground_truth": ["Signal Transduction"], "concept_id": 368, "hop_depth": 1}
+{"id": "bioinformatics_T2_37", "domain": "bioinformatics", "type": "T2_dependency", "query": "What are the prerequisites for Protein Data Bank?", "ground_truth": ["Biological Databases", "Protein Structure"], "concept_id": 37, "hop_depth": 1}
+{"id": "bioinformatics_T2_25", "domain": "bioinformatics", "type": "T2_dependency", "query": "What are the prerequisites for Structural Variant?", "ground_truth": ["Mutations", "Chromosomes"], "concept_id": 25, "hop_depth": 1}
+{"id": "bioinformatics_T2_346", "domain": "bioinformatics", "type": "T2_dependency", "query": "What are the prerequisites for Bipartite Metabolic Graph?", "ground_truth": ["Metabolic Network", "Bipartite Graphs"], "concept_id": 346, "hop_depth": 1}
+{"id": "bioinformatics_T2_120", "domain": "bioinformatics", "type": "T2_dependency", "query": "What are the prerequisites for RETURN Clause?", "ground_truth": ["Cypher Query Language"], "concept_id": 120, "hop_depth": 1}
+{"id": "bioinformatics_T2_403", "domain": "bioinformatics", "type": "T2_dependency", "query": "What are the prerequisites for Disease Ontology?", "ground_truth": ["Disease Ontology Database", "Ontology Structure"], "concept_id": 403, "hop_depth": 1}
+{"id": "bioinformatics_T2_153", "domain": "bioinformatics", "type": "T2_dependency", "query": "What are the prerequisites for Scoring Matrices?", "ground_truth": ["Sequence Alignment"], "concept_id": 153, "hop_depth": 1}
+{"id": "bioinformatics_T2_445", "domain": "bioinformatics", "type": "T2_dependency", "query": "What are the prerequisites for Patient Similarity Network?", "ground_truth": ["Multi-Omics Integration", "Undirected Graphs"], "concept_id": 445, "hop_depth": 1}
+{"id": "bioinformatics_T2_123", "domain": "bioinformatics", "type": "T2_dependency", "query": "What are the prerequisites for Graph Pattern Matching?", "ground_truth": ["Cypher Query Language", "MATCH Clause"], "concept_id": 123, "hop_depth": 1}
+{"id": "bioinformatics_T2_452", "domain": "bioinformatics", "type": "T2_dependency", "query": "What are the prerequisites for NetworkX?", "ground_truth": ["Python for Bioinformatics", "Nodes and Edges"], "concept_id": 452, "hop_depth": 1}
+{"id": "bioinformatics_T2_53", "domain": "bioinformatics", "type": "T2_dependency", "query": "What are the prerequisites for REST APIs for Biology?", "ground_truth": ["Programmatic Database Access"], "concept_id": 53, "hop_depth": 1}
+{"id": "bioinformatics_T2_201", "domain": "bioinformatics", "type": "T2_dependency", "query": "What are the prerequisites for Graph Model for Evolution?", "ground_truth": ["Phylogenetic Networks", "Graph Schema Design"], "concept_id": 201, "hop_depth": 1}
+{"id": "bioinformatics_T2_146", "domain": "bioinformatics", "type": "T2_dependency", "query": "What are the prerequisites for Sequence Alignment?", "ground_truth": ["Sequence Data"], "concept_id": 146, "hop_depth": 1}
+{"id": "bioinformatics_T2_239", "domain": "bioinformatics", "type": "T2_dependency", "query": "What are the prerequisites for Protein Surface Analysis?", "ground_truth": ["Tertiary Structure"], "concept_id": 239, "hop_depth": 1}
+{"id": "bioinformatics_T2_333", "domain": "bioinformatics", "type": "T2_dependency", "query": "What are the prerequisites for Bayesian Network Model?", "ground_truth": ["Gene Regulatory Network", "Bayesian Inference"], "concept_id": 333, "hop_depth": 1}
+{"id": "bioinformatics_T2_435", "domain": "bioinformatics", "type": "T2_dependency", "query": "What are the prerequisites for Leiden Algorithm?", "ground_truth": ["Community Detection"], "concept_id": 435, "hop_depth": 1}
+{"id": "bioinformatics_T2_193", "domain": "bioinformatics", "type": "T2_dependency", "query": "What are the prerequisites for Molecular Clock?", "ground_truth": ["Substitution Rate"], "concept_id": 193, "hop_depth": 1}
+{"id": "bioinformatics_T2_86", "domain": "bioinformatics", "type": "T2_dependency", "query": "What are the prerequisites for Centrality Measures?", "ground_truth": ["Graph Properties"], "concept_id": 86, "hop_depth": 1}
+{"id": "bioinformatics_T2_196", "domain": "bioinformatics", "type": "T2_dependency", "query": "What are the prerequisites for Phylogenetic Networks?", "ground_truth": ["Phylogenetic Tree", "Nodes and Edges"], "concept_id": 196, "hop_depth": 1}
+{"id": "bioinformatics_T2_188", "domain": "bioinformatics", "type": "T2_dependency", "query": "What are the prerequisites for Maximum Likelihood Method?", "ground_truth": ["Phylogenetic Tree", "Substitution Model"], "concept_id": 188, "hop_depth": 1}
+{"id": "bioinformatics_T2_110", "domain": "bioinformatics", "type": "T2_dependency", "query": "What are the prerequisites for Edge List Representation?", "ground_truth": ["Nodes and Edges"], "concept_id": 110, "hop_depth": 1}
+{"id": "bioinformatics_T2_351", "domain": "bioinformatics", "type": "T2_dependency", "query": "What are the prerequisites for Constraint-Based Modeling?", "ground_truth": ["Flux Balance Analysis"], "concept_id": 351, "hop_depth": 1}
+{"id": "bioinformatics_T2_140", "domain": "bioinformatics", "type": "T2_dependency", "query": "What are the prerequisites for Query Profiling?", "ground_truth": ["Cypher Query Language"], "concept_id": 140, "hop_depth": 1}
+{"id": "bioinformatics_T2_367", "domain": "bioinformatics", "type": "T2_dependency", "query": "What are the prerequisites for Signal Transduction?", "ground_truth": ["Cell Signaling Cascade"], "concept_id": 367, "hop_depth": 1}
+{"id": "bioinformatics_T2_357", "domain": "bioinformatics", "type": "T2_dependency", "query": "What are the prerequisites for Essential Reaction?", "ground_truth": ["Genome-Scale Model", "Flux Balance Analysis"], "concept_id": 357, "hop_depth": 1}
+{"id": "bioinformatics_T2_339", "domain": "bioinformatics", "type": "T2_dependency", "query": "What are the prerequisites for Alternative Splicing?", "ground_truth": ["RNA Structure", "Transcription"], "concept_id": 339, "hop_depth": 1}
+{"id": "bioinformatics_T2_38", "domain": "bioinformatics", "type": "T2_dependency", "query": "What are the prerequisites for Ensembl?", "ground_truth": ["Biological Databases", "Genome"], "concept_id": 38, "hop_depth": 1}
+{"id": "bioinformatics_T2_318", "domain": "bioinformatics", "type": "T2_dependency", "query": "What are the prerequisites for Statistical Testing for DE?", "ground_truth": ["Differential Expression"], "concept_id": 318, "hop_depth": 1}
+{"id": "bioinformatics_T2_90", "domain": "bioinformatics", "type": "T2_dependency", "query": "What are the prerequisites for Eigenvector Centrality?", "ground_truth": ["Centrality Measures"], "concept_id": 90, "hop_depth": 1}
+{"id": "bioinformatics_T3_71_99", "domain": "bioinformatics", "type": "T3_path", "query": "What is the prerequisite chain from Graph Theory to Graph Density?", "ground_truth": ["Graph Density", "Graph Properties", "Nodes and Edges", "Graph Theory"], "concept_id": 99, "hop_depth": 3, "path_ids": [99, 81, 72, 71]}
+{"id": "bioinformatics_T3_71_210", "domain": "bioinformatics", "type": "T3_path", "query": "What is the prerequisite chain from Graph Theory to Ancestral Reconstruction?", "ground_truth": ["Ancestral Reconstruction", "Phylogenetic Tree", "Directed Graphs", "Nodes and Edges", "Graph Theory"], "concept_id": 210, "hop_depth": 4, "path_ids": [210, 181, 73, 72, 71]}
+{"id": "bioinformatics_T3_71_348", "domain": "bioinformatics", "type": "T3_path", "query": "What is the prerequisite chain from Graph Theory to Reactome Pathways?", "ground_truth": ["Reactome Pathways", "Metabolic Pathway", "Metabolic Network", "Bipartite Graphs", "Nodes and Edges", "Graph Theory"], "concept_id": 348, "hop_depth": 5, "path_ids": [348, 345, 341, 76, 72, 71]}
+{"id": "bioinformatics_T3_71_434", "domain": "bioinformatics", "type": "T3_path", "query": "What is the prerequisite chain from Graph Theory to Louvain Algorithm?", "ground_truth": ["Louvain Algorithm", "Community Detection", "Connected Components", "Graph Properties", "Nodes and Edges", "Graph Theory"], "concept_id": 434, "hop_depth": 5, "path_ids": [434, 433, 92, 81, 72, 71]}
+{"id": "bioinformatics_T3_1_306", "domain": "bioinformatics", "type": "T3_path", "query": "What is the prerequisite chain from Bioinformatics to Structural Variant Calling?", "ground_truth": ["Structural Variant Calling", "Structural Variant", "Mutations", "DNA Structure", "Central Dogma", "Bioinformatics"], "concept_id": 306, "hop_depth": 5, "path_ids": [306, 25, 22, 4, 3, 1]}
+{"id": "bioinformatics_T3_1_69", "domain": "bioinformatics", "type": "T3_path", "query": "What is the prerequisite chain from Bioinformatics to Data Format Conversion?", "ground_truth": ["Data Format Conversion", "FASTA Format", "Sequence Data", "DNA Structure", "Central Dogma", "Bioinformatics"], "concept_id": 69, "hop_depth": 5, "path_ids": [69, 56, 15, 4, 3, 1]}
+{"id": "bioinformatics_T3_71_387", "domain": "bioinformatics", "type": "T3_path", "query": "What is the prerequisite chain from Graph Theory to Cancer Network Analysis?", "ground_truth": ["Cancer Network Analysis", "Protein Interaction Network", "Nodes and Edges", "Graph Theory"], "concept_id": 387, "hop_depth": 3, "path_ids": [387, 251, 72, 71]}
+{"id": "bioinformatics_T3_71_142", "domain": "bioinformatics", "type": "T3_path", "query": "What is the prerequisite chain from Graph Theory to Graph Partitioning?", "ground_truth": ["Graph Partitioning", "Distributed Graph Databases", "Graph Database", "Labeled Property Graph", "Nodes and Edges", "Graph Theory"], "concept_id": 142, "hop_depth": 5, "path_ids": [142, 141, 111, 77, 72, 71]}
+{"id": "bioinformatics_T3_71_449", "domain": "bioinformatics", "type": "T3_path", "query": "What is the prerequisite chain from Graph Theory to Network-Based Biomarkers?", "ground_truth": ["Network-Based Biomarkers", "Patient Stratification", "Patient Similarity Network", "Undirected Graphs", "Nodes and Edges", "Graph Theory"], "concept_id": 449, "hop_depth": 5, "path_ids": [449, 448, 445, 74, 72, 71]}
+{"id": "bioinformatics_T3_1_344", "domain": "bioinformatics", "type": "T3_path", "query": "What is the prerequisite chain from Bioinformatics to Enzyme Kinetics?", "ground_truth": ["Enzyme Kinetics", "Enzyme", "Protein Structure", "Central Dogma", "Bioinformatics"], "concept_id": 344, "hop_depth": 4, "path_ids": [344, 343, 6, 3, 1]}
+{"id": "bioinformatics_T3_71_142", "domain": "bioinformatics", "type": "T3_path", "query": "What is the prerequisite chain from Graph Theory to Graph Partitioning?", "ground_truth": ["Graph Partitioning", "Distributed Graph Databases", "Graph Database", "Labeled Property Graph", "Nodes and Edges", "Graph Theory"], "concept_id": 142, "hop_depth": 5, "path_ids": [142, 141, 111, 77, 72, 71]}
+{"id": "bioinformatics_T3_1_333", "domain": "bioinformatics", "type": "T3_path", "query": "What is the prerequisite chain from Bioinformatics to Bayesian Network Model?", "ground_truth": ["Bayesian Network Model", "Gene Regulatory Network", "Transcription Factor", "DNA Structure", "Central Dogma", "Bioinformatics"], "concept_id": 333, "hop_depth": 5, "path_ids": [333, 325, 320, 4, 3, 1]}
+{"id": "bioinformatics_T3_71_459", "domain": "bioinformatics", "type": "T3_path", "query": "What is the prerequisite chain from Graph Theory to Cytoscape API?", "ground_truth": ["Cytoscape API", "Cytoscape Tool", "Graph Visualization", "Nodes and Edges", "Graph Theory"], "concept_id": 459, "hop_depth": 4, "path_ids": [459, 441, 439, 72, 71]}
+{"id": "bioinformatics_T3_1_298", "domain": "bioinformatics", "type": "T3_path", "query": "What is the prerequisite chain from Bioinformatics to Gene Prediction?", "ground_truth": ["Gene Prediction", "Genome Annotation", "Gene", "DNA Structure", "Central Dogma", "Bioinformatics"], "concept_id": 298, "hop_depth": 5, "path_ids": [298, 297, 10, 4, 3, 1]}
+{"id": "bioinformatics_T3_1_300", "domain": "bioinformatics", "type": "T3_path", "query": "What is the prerequisite chain from Bioinformatics to Short Reads?", "ground_truth": ["Short Reads", "Next-Gen Sequencing", "Sequence Data", "DNA Structure", "Central Dogma", "Bioinformatics"], "concept_id": 300, "hop_depth": 5, "path_ids": [300, 299, 15, 4, 3, 1]}
+{"id": "bioinformatics_T3_71_344", "domain": "bioinformatics", "type": "T3_path", "query": "What is the prerequisite chain from Graph Theory to Enzyme Kinetics?", "ground_truth": ["Enzyme Kinetics", "Enzyme", "Metabolic Network", "Bipartite Graphs", "Nodes and Edges", "Graph Theory"], "concept_id": 344, "hop_depth": 5, "path_ids": [344, 343, 341, 76, 72, 71]}
+{"id": "bioinformatics_T3_71_465", "domain": "bioinformatics", "type": "T3_path", "query": "What is the prerequisite chain from Graph Theory to Capstone Project Design?", "ground_truth": ["Capstone Project Design", "Graph Data Model Design", "Graph Schema Design", "Labeled Property Graph", "Nodes and Edges", "Graph Theory"], "concept_id": 465, "hop_depth": 5, "path_ids": [465, 466, 127, 77, 72, 71]}
+{"id": "bioinformatics_T3_71_438", "domain": "bioinformatics", "type": "T3_path", "query": "What is the prerequisite chain from Graph Theory to Spectral Clustering?", "ground_truth": ["Spectral Clustering", "Adjacency Matrix", "Nodes and Edges", "Graph Theory"], "concept_id": 438, "hop_depth": 3, "path_ids": [438, 109, 72, 71]}
+{"id": "bioinformatics_T3_71_442", "domain": "bioinformatics", "type": "T3_path", "query": "What is the prerequisite chain from Graph Theory to Force-Directed Layout?", "ground_truth": ["Force-Directed Layout", "Graph Visualization", "Nodes and Edges", "Graph Theory"], "concept_id": 442, "hop_depth": 3, "path_ids": [442, 439, 72, 71]}
+{"id": "bioinformatics_T3_71_379", "domain": "bioinformatics", "type": "T3_path", "query": "What is the prerequisite chain from Graph Theory to Drug Target Validation?", "ground_truth": ["Drug Target Validation", "Drug Target", "Network Medicine", "Protein Interaction Network", "Nodes and Edges", "Graph Theory"], "concept_id": 379, "hop_depth": 5, "path_ids": [379, 378, 374, 251, 72, 71]}
+{"id": "bioinformatics_T3_1_61", "domain": "bioinformatics", "type": "T3_path", "query": "What is the prerequisite chain from Bioinformatics to PDB File Format?", "ground_truth": ["PDB File Format", "Protein Data Bank", "Protein Structure", "Central Dogma", "Bioinformatics"], "concept_id": 61, "hop_depth": 4, "path_ids": [61, 37, 6, 3, 1]}
+{"id": "bioinformatics_T3_1_69", "domain": "bioinformatics", "type": "T3_path", "query": "What is the prerequisite chain from Bioinformatics to Data Format Conversion?", "ground_truth": ["Data Format Conversion", "FASTA Format", "Sequence Data", "DNA Structure", "Central Dogma", "Bioinformatics"], "concept_id": 69, "hop_depth": 5, "path_ids": [69, 56, 15, 4, 3, 1]}
+{"id": "bioinformatics_T3_71_465", "domain": "bioinformatics", "type": "T3_path", "query": "What is the prerequisite chain from Graph Theory to Capstone Project Design?", "ground_truth": ["Capstone Project Design", "Graph Data Model Design", "Graph Schema Design", "Labeled Property Graph", "Nodes and Edges", "Graph Theory"], "concept_id": 465, "hop_depth": 5, "path_ids": [465, 466, 127, 77, 72, 71]}
+{"id": "bioinformatics_T3_71_200", "domain": "bioinformatics", "type": "T3_path", "query": "What is the prerequisite chain from Graph Theory to Incomplete Lineage Sorting?", "ground_truth": ["Incomplete Lineage Sorting", "Gene Tree vs Species Tree", "Phylogenetic Tree", "Directed Graphs", "Nodes and Edges", "Graph Theory"], "concept_id": 200, "hop_depth": 5, "path_ids": [200, 212, 181, 73, 72, 71]}
+{"id": "bioinformatics_T3_71_209", "domain": "bioinformatics", "type": "T3_path", "query": "What is the prerequisite chain from Graph Theory to Robinson-Foulds Distance?", "ground_truth": ["Robinson-Foulds Distance", "Tree Topology Comparison", "Phylogenetic Tree", "Directed Graphs", "Nodes and Edges", "Graph Theory"], "concept_id": 209, "hop_depth": 5, "path_ids": [209, 208, 181, 73, 72, 71]}
+{"id": "bioinformatics_T4_FOUND", "domain": "bioinformatics", "type": "T4_aggregate", "query": "List all FOUND concepts in this knowledge graph", "ground_truth": ["Bioinformatics", "Computational Biology", "Central Dogma", "DNA Structure", "RNA Structure", "Protein Structure", "Amino Acids", "Nucleotides", "Codons", "Gene", "Genome", "Transcription", "Translation", "Gene Expression", "Sequence Data", "Molecular Biology", "Cell Biology Basics", "Genetic Code", "Open Reading Frame", "Complementary Base Pairing", "Chromosomes", "Mutations", "Single Nucleotide Polymorphism", "Insertion and Deletion", "Structural Variant", "Copy Number Variation", "Epigenetics", "DNA Methylation", "Histone Modification", "Central Dogma Exceptions"], "taxonomy_id": "FOUND", "hop_depth": 0}
+{"id": "bioinformatics_T4_DBAS", "domain": "bioinformatics", "type": "T4_aggregate", "query": "List all DBAS concepts in this knowledge graph", "ground_truth": ["Biological Databases", "NCBI", "GenBank Database", "UniProt", "Swiss-Prot", "TrEMBL", "Protein Data Bank", "Ensembl", "KEGG Database", "Reactome Database", "BioGRID Database", "STRING Database", "IntAct Database", "COSMIC Database", "Gene Ontology Database", "Disease Ontology Database", "Human Phenotype Ontology DB", "BioCyc Database", "OMIM Database", "Hetionet Database", "Database Cross-References", "Programmatic Database Access", "REST APIs for Biology", "Batch Data Download", "Data Provenance"], "taxonomy_id": "DBAS", "hop_depth": 0}
+{"id": "bioinformatics_T4_DFMT", "domain": "bioinformatics", "type": "T4_aggregate", "query": "List all DFMT concepts in this knowledge graph", "ground_truth": ["FASTA Format", "FASTQ Format", "GenBank Format", "GFF3 Format", "OWL Format", "PDB File Format", "VCF Format", "SAM and BAM Format", "BED Format", "SBML Format", "BioPAX Format", "CSV for Bioinformatics", "JSON for Bioinformatics", "Data Format Conversion", "Data Quality Control"], "taxonomy_id": "DFMT", "hop_depth": 0}
+{"id": "bioinformatics_T4_GRTH", "domain": "bioinformatics", "type": "T4_aggregate", "query": "List all GRTH concepts in this knowledge graph", "ground_truth": ["Graph Theory", "Nodes and Edges", "Directed Graphs", "Undirected Graphs", "Weighted Graphs", "Bipartite Graphs", "Labeled Property Graph", "Multigraph", "Hypergraph", "Subgraph", "Graph Properties", "Degree Distribution", "In-Degree", "Out-Degree", "Clustering Coefficient", "Centrality Measures", "Degree Centrality", "Betweenness Centrality", "Closeness Centrality", "Eigenvector Centrality", "PageRank", "Connected Components", "Strongly Connected Comp", "Graph Traversal", "Breadth-First Search", "Depth-First Search", "Shortest Path Algorithms", "Dijkstra Algorithm", "Graph Density", "Graph Diameter", "Scale-Free Networks", "Small-World Networks", "Power-Law Distribution", "Random Graph Models", "Erdos-Renyi Model", "Barabasi-Albert Model", "Network Motifs", "Graph Isomorphism", "Adjacency Matrix", "Edge List Representation", "Community Detection", "Louvain Algorithm", "Leiden Algorithm", "Modularity Score", "Graph Clustering", "Spectral Clustering", "Graph Visualization", "Vis-Network Library", "Cytoscape Tool", "Force-Directed Layout", "Hierarchical Layout", "Network Layout Algorithms"], "taxonomy_id": "GRTH", "hop_depth": 0}
+{"id": "bioinformatics_T4_GRDB", "domain": "bioinformatics", "type": "T4_aggregate", "query": "List all GRDB concepts in this knowledge graph", "ground_truth": ["Graph Database", "Relational Database", "Graph vs Relational Model", "Neo4j", "Memgraph", "Cypher Query Language", "GQL Query Language", "MATCH Clause", "WHERE Clause", "RETURN Clause", "CREATE Clause", "MERGE Clause", "Graph Pattern Matching", "Variable-Length Paths", "Path Queries", "Aggregation in Cypher", "Graph Schema Design", "Node Labels", "Relationship Types", "Property Keys", "Index and Constraints", "RDF Triple Model", "Subject-Predicate-Object", "SPARQL Query Language", "LPG vs RDF Comparison", "Graph Data Loading", "CSV Import to Graph DB", "ETL for Graph Databases", "Graph Query Optimization", "Query Profiling", "Distributed Graph Databases", "Graph Partitioning", "Graph Scalability", "Graph Transactions", "Graph Access Control", "Graph Model for Similarity", "Graph Model for Evolution", "Graph Model for Contacts", "Graph Model for PPIs", "Graph Model for Variants", "Graph Model for Regulation", "Graph Model for Metabolism", "Graph Model for Repurposing", "Graph Model for Knowledge", "Graph Model for Multi-Omics"], "taxonomy_id": "GRDB", "hop_depth": 0}
+{"id": "bioinformatics_T4_SEQA", "domain": "bioinformatics", "type": "T4_aggregate", "query": "List all SEQA concepts in this knowledge graph", "ground_truth": ["Sequence Alignment", "Pairwise Alignment", "Global Alignment", "Local Alignment", "Smith-Waterman Algorithm", "Needleman-Wunsch Algorithm", "Dynamic Programming", "Scoring Matrices", "BLOSUM Matrix", "PAM Matrix", "Substitution Model", "Gap Penalties", "Affine Gap Penalty", "BLAST", "BLAST E-Value", "BLAST Heuristics", "PSI-BLAST", "Sequence Homology", "Orthologs", "Paralogs", "Sequence Identity", "Sequence Similarity", "Sequence Similarity Network", "Multiple Sequence Alignment", "Clustal", "MUSCLE Aligner", "Progressive Alignment", "Consensus Sequence", "Sequence Profile", "Hidden Markov Model", "Profile HMM", "Sequence Motif", "Regular Expressions", "Motif Discovery"], "taxonomy_id": "SEQA", "hop_depth": 0}
+{"id": "bioinformatics_T4_PHYL", "domain": "bioinformatics", "type": "T4_aggregate", "query": "List all PHYL concepts in this knowledge graph", "ground_truth": ["Phylogenetic Tree", "Phylogenetics", "Molecular Phylogenetics", "Distance Matrix", "Neighbor-Joining Method", "UPGMA Method", "Maximum Parsimony", "Maximum Likelihood Method", "Bayesian Inference", "Markov Chain Monte Carlo", "Bootstrap Analysis", "Branch Support Values", "Molecular Clock", "Substitution Rate", "Trees as DAGs", "Phylogenetic Networks", "Reticulate Evolution", "Horizontal Gene Transfer", "Recombination", "Incomplete Lineage Sorting", "Cladogram", "Phylogram", "Monophyletic Group", "Paraphyletic Group", "Outgroup", "Rooted vs Unrooted Trees", "Tree Topology Comparison", "Robinson-Foulds Distance", "Ancestral Reconstruction", "Divergence Time Estimation", "Gene Tree vs Species Tree", "Coalescent Theory", "Phylogenomics", "Comparative Genomics"], "taxonomy_id": "PHYL", "hop_depth": 0}
+{"id": "bioinformatics_T4_STRU", "domain": "bioinformatics", "type": "T4_aggregate", "query": "List all STRU concepts in this knowledge graph", "ground_truth": ["Primary Structure", "Secondary Structure", "Alpha Helix", "Beta Sheet", "Tertiary Structure", "Quaternary Structure", "Protein Folding", "Protein Folding Problem", "Homology Modeling", "Threading", "Ab Initio Prediction", "AlphaFold", "AlphaFold Database", "Protein Contact Map", "Contact Map as Graph", "Residue Interaction Network", "Structural Alignment", "RMSD", "Protein Domain", "Domain Classification", "SCOP Database", "Pfam Database", "Protein Surface Analysis", "Binding Site Prediction", "Molecular Docking", "Ligand-Protein Interaction", "Drug-Likeness", "ADMET Properties", "Protein-Ligand Graph", "Molecular Fingerprints", "Chemical Similarity", "Structure-Activity Relation", "Protein Function Inference", "Structural Genomics"], "taxonomy_id": "STRU", "hop_depth": 0}
+{"id": "bioinformatics_T4_PPIS", "domain": "bioinformatics", "type": "T4_aggregate", "query": "List all PPIS concepts in this knowledge graph", "ground_truth": ["Protein Interaction Network", "Interactome", "Yeast Two-Hybrid", "Co-Immunoprecipitation", "Affinity Purification MS", "Cross-Linking Mass Spec", "PPI Confidence Scoring", "Binary vs Complex PPIs", "Network Hubs", "Network Bottlenecks", "Network Modules", "Hub-and-Spoke Topology", "Date Hubs vs Party Hubs", "Essential Proteins", "Protein Complex Detection", "Clique Detection", "Dense Subgraph Mining", "Network Rewiring", "Dynamic PPI Networks", "Tissue-Specific PPIs", "Host-Pathogen PPIs", "Viral Interactome", "PPI Prediction Methods", "Interaction Domain Pairs", "Co-Evolution Analysis", "Network Alignment", "Network Comparison", "Graphlet Analysis", "Network Centrality in PPIs"], "taxonomy_id": "PPIS", "hop_depth": 0}
+{"id": "bioinformatics_T4_GENO", "domain": "bioinformatics", "type": "T4_aggregate", "query": "List all GENO concepts in this knowledge graph", "ground_truth": ["Genome Assembly", "De Bruijn Graph", "K-mer", "K-mer Spectrum", "Contig", "Scaffold", "N50 Metric", "Assembly Quality Metrics", "Reference Genome", "Reference Bias", "Pangenome", "Pangenome Graph", "Variation Graph", "VG Toolkit", "Read Mapping to Graphs", "Genome Annotation", "Gene Prediction", "Next-Gen Sequencing", "Short Reads", "Long Reads", "Sequencing Depth", "Coverage", "Variant Calling", "SNP Calling", "Structural Variant Calling", "Genotyping", "Haplotype", "Phasing", "Population Reference Graph"], "taxonomy_id": "GENO", "hop_depth": 0}
+{"id": "bioinformatics_T4_TRNS", "domain": "bioinformatics", "type": "T4_aggregate", "query": "List all TRNS concepts in this knowledge graph", "ground_truth": ["Transcriptome", "RNA-Seq Pipeline", "Read Quality Trimming", "Read Alignment", "Transcript Quantification", "Differential Expression", "Fold Change", "Statistical Testing for DE", "False Discovery Rate", "Transcription Factor", "Promoter Region", "Enhancer Region", "Cis-Regulatory Element", "Operon", "Gene Regulatory Network", "Co-Expression Network", "WGCNA", "ARACNE", "GENIE3", "Mutual Information", "Network Inference Methods", "Boolean Network Model", "Bayesian Network Model", "Single-Cell RNA-Seq", "Cell Type Clustering", "Trajectory Analysis", "Spatial Transcriptomics", "Alternative Splicing", "Non-Coding RNA"], "taxonomy_id": "TRNS", "hop_depth": 0}
+{"id": "bioinformatics_T4_PATH", "domain": "bioinformatics", "type": "T4_aggregate", "query": "List all PATH concepts in this knowledge graph", "ground_truth": ["Metabolic Network", "Metabolite", "Enzyme", "Enzyme Kinetics", "Metabolic Pathway", "Bipartite Metabolic Graph", "KEGG Pathways", "Reactome Pathways", "BioCyc Pathways", "Flux Balance Analysis", "Constraint-Based Modeling", "Stoichiometric Matrix", "Objective Function", "Metabolic Flux", "Genome-Scale Model", "Essential Reaction", "Minimal Growth Medium", "Metabolic Engineering", "Synthetic Biology", "Pathway Enrichment", "Metabolomics", "Mass Spec for Metabolomics", "Metabolic Network Compare", "Metabolic Graph Alignment", "Cell Signaling Cascade", "Signal Transduction", "Receptor", "Kinase Cascade", "Second Messenger", "Directed Signaling Graph", "Feedback Loop", "Feed-Forward Loop", "Network Medicine", "Disease Module", "Network Proximity", "Guilt by Association", "Drug Target", "Drug Target Validation", "Drug Repurposing", "Drug-Target-Disease Graph", "Pharmacogenomics", "Cancer Driver Genes", "Tumor Suppressor Gene", "Oncogene", "Cancer Network Analysis", "Precision Medicine", "Biomarker Discovery", "Clinical Network Analysis", "Side Effect Prediction", "Drug-Drug Interaction Graph", "Adverse Event Network", "Comorbidity Network", "Disease Gene Prioritization"], "taxonomy_id": "PATH", "hop_depth": 0}
+{"id": "bioinformatics_T4_KNOW", "domain": "bioinformatics", "type": "T4_aggregate", "query": "List all KNOW concepts in this knowledge graph", "ground_truth": ["Knowledge Graph", "Biomedical Knowledge Graph", "Gene Ontology", "GO Molecular Function", "GO Biological Process", "GO Cellular Component", "GO Term Enrichment", "Disease Ontology", "Human Phenotype Ontology", "Ontology Structure", "Ontology Reasoning", "Semantic Similarity", "Heterogeneous Data", "Data Integration", "Schema Mapping", "Entity Resolution", "Graph Embeddings", "Node2Vec", "TransE", "Knowledge Graph Embedding", "Link Prediction", "Triple Classification", "Relation Extraction", "Named Entity Recognition", "Text Mining for Biology", "Graph Neural Networks", "Message Passing", "GNN for Molecules", "Hetionet", "Multi-Omics Integration", "Genomics Layer", "Transcriptomics Layer", "Proteomics Layer", "Metabolomics Layer", "Unified Omics Graph", "Patient Similarity Network", "Clinical Data Graph", "Survival Analysis", "Patient Stratification", "Network-Based Biomarkers"], "taxonomy_id": "KNOW", "hop_depth": 0}
+{"id": "bioinformatics_T4_TOOL", "domain": "bioinformatics", "type": "T4_aggregate", "query": "List all TOOL concepts in this knowledge graph", "ground_truth": ["Python for Bioinformatics", "Biopython", "NetworkX", "Pandas for Bioinformatics", "Scikit-Learn", "Jupyter Notebooks", "Matplotlib", "Seaborn", "Neo4j Python Driver", "Cytoscape API", "Data Wrangling", "Reproducible Analysis", "Version Control for Science", "Workflow Managers", "Conda Environments", "Capstone Project Design", "Graph Data Model Design", "Antibiotic Resistance Graph", "Resistance Gene Network", "Mobile Genetic Elements", "Rare Disease Knowledge Graph", "Phenotype-Gene Mapping", "Metabolic Model Comparison", "Cross-Species Graph Align", "Protein Function Predict", "GO Annotation Prediction", "Multi-Omics Stratification", "Patient Subgroup Discovery", "Graph-Based Discovery", "Bench to Bedside Pipeline", "Future of Graph Bioinform"], "taxonomy_id": "TOOL", "hop_depth": 0}
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+{"id": "bioinformatics_T5_360_359", "domain": "bioinformatics", "type": "T5_cross_concept", "query": "How does Synthetic Biology relate to Metabolic Engineering?", "ground_truth": ["Synthetic Biology", "Metabolic Engineering"], "concept_id_a": 360, "concept_id_b": 359, "hop_depth": 1}
+{"id": "bioinformatics_T5_177_176", "domain": "bioinformatics", "type": "T5_cross_concept", "query": "How does Profile HMM relate to Hidden Markov Model?", "ground_truth": ["Profile HMM", "Hidden Markov Model"], "concept_id_a": 177, "concept_id_b": 176, "hop_depth": 1}
+{"id": "bioinformatics_T5_158_157", "domain": "bioinformatics", "type": "T5_cross_concept", "query": "How does Affine Gap Penalty relate to Gap Penalties?", "ground_truth": ["Affine Gap Penalty", "Gap Penalties"], "concept_id_a": 158, "concept_id_b": 157, "hop_depth": 1}
+{"id": "bioinformatics_T5_353_350", "domain": "bioinformatics", "type": "T5_cross_concept", "query": "How does Objective Function relate to Flux Balance Analysis?", "ground_truth": ["Objective Function", "Flux Balance Analysis"], "concept_id_a": 353, "concept_id_b": 350, "hop_depth": 1}
+{"id": "bioinformatics_T5_276_251", "domain": "bioinformatics", "type": "T5_cross_concept", "query": "How does Co-Evolution Analysis relate to Protein Interaction Network?", "ground_truth": ["Co-Evolution Analysis", "Protein Interaction Network"], "concept_id_a": 276, "concept_id_b": 251, "hop_depth": 1}
+{"id": "bioinformatics_T5_244_243", "domain": "bioinformatics", "type": "T5_cross_concept", "query": "How does ADMET Properties relate to Drug-Likeness?", "ground_truth": ["ADMET Properties", "Drug-Likeness"], "concept_id_a": 244, "concept_id_b": 243, "hop_depth": 1}
+{"id": "bioinformatics_T5_181_73", "domain": "bioinformatics", "type": "T5_cross_concept", "query": "How does Phylogenetic Tree relate to Directed Graphs?", "ground_truth": ["Phylogenetic Tree", "Directed Graphs"], "concept_id_a": 181, "concept_id_b": 73, "hop_depth": 1}
+{"id": "bioinformatics_T5_177_175", "domain": "bioinformatics", "type": "T5_cross_concept", "query": "How does Profile HMM relate to Sequence Profile?", "ground_truth": ["Profile HMM", "Sequence Profile"], "concept_id_a": 177, "concept_id_b": 175, "hop_depth": 1}
+{"id": "bioinformatics_T5_18_3", "domain": "bioinformatics", "type": "T5_cross_concept", "query": "How does Genetic Code relate to Central Dogma?", "ground_truth": ["Genetic Code", "Central Dogma"], "concept_id_a": 18, "concept_id_b": 3, "hop_depth": 1}
+{"id": "bioinformatics_T5_151_148", "domain": "bioinformatics", "type": "T5_cross_concept", "query": "How does Needleman-Wunsch Algorithm relate to Global Alignment?", "ground_truth": ["Needleman-Wunsch Algorithm", "Global Alignment"], "concept_id_a": 151, "concept_id_b": 148, "hop_depth": 1}
+{"id": "bioinformatics_T5_311_14", "domain": "bioinformatics", "type": "T5_cross_concept", "query": "How does Transcriptome relate to Gene Expression?", "ground_truth": ["Transcriptome", "Gene Expression"], "concept_id_a": 311, "concept_id_b": 14, "hop_depth": 1}
+{"id": "bioinformatics_T5_261_92", "domain": "bioinformatics", "type": "T5_cross_concept", "query": "How does Network Modules relate to Connected Components?", "ground_truth": ["Network Modules", "Connected Components"], "concept_id_a": 261, "concept_id_b": 92, "hop_depth": 1}
+{"id": "bioinformatics_T5_223_222", "domain": "bioinformatics", "type": "T5_cross_concept", "query": "How does Protein Folding Problem relate to Protein Folding?", "ground_truth": ["Protein Folding Problem", "Protein Folding"], "concept_id_a": 223, "concept_id_b": 222, "hop_depth": 1}
+{"id": "bioinformatics_T5_50_31", "domain": "bioinformatics", "type": "T5_cross_concept", "query": "How does Hetionet Database relate to Biological Databases?", "ground_truth": ["Hetionet Database", "Biological Databases"], "concept_id_a": 50, "concept_id_b": 31, "hop_depth": 1}
+{"id": "bioinformatics_T5_276_167", "domain": "bioinformatics", "type": "T5_cross_concept", "query": "How does Co-Evolution Analysis relate to Sequence Similarity?", "ground_truth": ["Co-Evolution Analysis", "Sequence Similarity"], "concept_id_a": 276, "concept_id_b": 167, "hop_depth": 1}
+{"id": "bioinformatics_T5_477_476", "domain": "bioinformatics", "type": "T5_cross_concept", "query": "How does Patient Subgroup Discovery relate to Multi-Omics Stratification?", "ground_truth": ["Patient Subgroup Discovery", "Multi-Omics Stratification"], "concept_id_a": 477, "concept_id_b": 476, "hop_depth": 1}
+{"id": "bioinformatics_T5_384_44", "domain": "bioinformatics", "type": "T5_cross_concept", "query": "How does Cancer Driver Genes relate to COSMIC Database?", "ground_truth": ["Cancer Driver Genes", "COSMIC Database", "Mutations"], "concept_id_a": 384, "concept_id_b": 44, "hop_depth": 1}
+{"id": "bioinformatics_T5_311_5", "domain": "bioinformatics", "type": "T5_cross_concept", "query": "How does Transcriptome relate to RNA Structure?", "ground_truth": ["Transcriptome", "RNA Structure"], "concept_id_a": 311, "concept_id_b": 5, "hop_depth": 1}
+{"id": "bioinformatics_T5_400_398", "domain": "bioinformatics", "type": "T5_cross_concept", "query": "How does GO Biological Process relate to Gene Ontology?", "ground_truth": ["GO Biological Process", "Gene Ontology"], "concept_id_a": 400, "concept_id_b": 398, "hop_depth": 1}
+{"id": "bioinformatics_T5_263_259", "domain": "bioinformatics", "type": "T5_cross_concept", "query": "How does Hub-and-Spoke Topology relate to Network Hubs?", "ground_truth": ["Hub-and-Spoke Topology", "Network Hubs"], "concept_id_a": 263, "concept_id_b": 259, "hop_depth": 1}
+{"id": "bioinformatics_T5_20_8", "domain": "bioinformatics", "type": "T5_cross_concept", "query": "How does Complementary Base Pairing relate to Nucleotides?", "ground_truth": ["Complementary Base Pairing", "Nucleotides", "DNA Structure"], "concept_id_a": 20, "concept_id_b": 8, "hop_depth": 1}
+{"id": "bioinformatics_T5_89_86", "domain": "bioinformatics", "type": "T5_cross_concept", "query": "How does Closeness Centrality relate to Centrality Measures?", "ground_truth": ["Closeness Centrality", "Centrality Measures"], "concept_id_a": 89, "concept_id_b": 86, "hop_depth": 1}
+{"id": "bioinformatics_T5_210_181", "domain": "bioinformatics", "type": "T5_cross_concept", "query": "How does Ancestral Reconstruction relate to Phylogenetic Tree?", "ground_truth": ["Ancestral Reconstruction", "Phylogenetic Tree"], "concept_id_a": 210, "concept_id_b": 181, "hop_depth": 1}
+{"id": "bioinformatics_T5_139_116", "domain": "bioinformatics", "type": "T5_cross_concept", "query": "How does Graph Query Optimization relate to Cypher Query Language?", "ground_truth": ["Graph Query Optimization", "Cypher Query Language"], "concept_id_a": 139, "concept_id_b": 116, "hop_depth": 1}
+{"id": "bioinformatics_T5_431_72", "domain": "bioinformatics", "type": "T5_cross_concept", "query": "How does Unified Omics Graph relate to Nodes and Edges?", "ground_truth": ["Unified Omics Graph", "Nodes and Edges"], "concept_id_a": 431, "concept_id_b": 72, "hop_depth": 1}
+{"id": "bioinformatics_T5_212_181", "domain": "bioinformatics", "type": "T5_cross_concept", "query": "How does Gene Tree vs Species Tree relate to Phylogenetic Tree?", "ground_truth": ["Gene Tree vs Species Tree", "Phylogenetic Tree"], "concept_id_a": 212, "concept_id_b": 181, "hop_depth": 1}
+{"id": "bioinformatics_T5_84_82", "domain": "bioinformatics", "type": "T5_cross_concept", "query": "How does Out-Degree relate to Degree Distribution?", "ground_truth": ["Out-Degree", "Degree Distribution"], "concept_id_a": 84, "concept_id_b": 82, "hop_depth": 1}
+{"id": "bioinformatics_T5_445_426", "domain": "bioinformatics", "type": "T5_cross_concept", "query": "How does Patient Similarity Network relate to Multi-Omics Integration?", "ground_truth": ["Patient Similarity Network", "Multi-Omics Integration"], "concept_id_a": 445, "concept_id_b": 426, "hop_depth": 1}
+{"id": "bioinformatics_T5_336_335", "domain": "bioinformatics", "type": "T5_cross_concept", "query": "How does Cell Type Clustering relate to Single-Cell RNA-Seq?", "ground_truth": ["Cell Type Clustering", "Single-Cell RNA-Seq"], "concept_id_a": 336, "concept_id_b": 335, "hop_depth": 1}
+{"id": "bioinformatics_T5_273_272", "domain": "bioinformatics", "type": "T5_cross_concept", "query": "How does Viral Interactome relate to Host-Pathogen PPIs?", "ground_truth": ["Viral Interactome", "Host-Pathogen PPIs"], "concept_id_a": 273, "concept_id_b": 272, "hop_depth": 1}
+{"id": "bioinformatics_T5_250_11", "domain": "bioinformatics", "type": "T5_cross_concept", "query": "How does Structural Genomics relate to Genome?", "ground_truth": ["Structural Genomics", "Genome"], "concept_id_a": 250, "concept_id_b": 11, "hop_depth": 1}
+{"id": "bioinformatics_T5_338_335", "domain": "bioinformatics", "type": "T5_cross_concept", "query": "How does Spatial Transcriptomics relate to Single-Cell RNA-Seq?", "ground_truth": ["Spatial Transcriptomics", "Single-Cell RNA-Seq"], "concept_id_a": 338, "concept_id_b": 335, "hop_depth": 1}
+{"id": "bioinformatics_T5_452_450", "domain": "bioinformatics", "type": "T5_cross_concept", "query": "How does NetworkX relate to Python for Bioinformatics?", "ground_truth": ["NetworkX", "Python for Bioinformatics"], "concept_id_a": 452, "concept_id_b": 450, "hop_depth": 1}
+{"id": "bioinformatics_T5_385_10", "domain": "bioinformatics", "type": "T5_cross_concept", "query": "How does Tumor Suppressor Gene relate to Gene?", "ground_truth": ["Tumor Suppressor Gene", "Gene"], "concept_id_a": 385, "concept_id_b": 10, "hop_depth": 1}
+{"id": "bioinformatics_T5_301_299", "domain": "bioinformatics", "type": "T5_cross_concept", "query": "How does Long Reads relate to Next-Gen Sequencing?", "ground_truth": ["Long Reads", "Next-Gen Sequencing"], "concept_id_a": 301, "concept_id_b": 299, "hop_depth": 1}
+{"id": "bioinformatics_T5_429_251", "domain": "bioinformatics", "type": "T5_cross_concept", "query": "How does Proteomics Layer relate to Protein Interaction Network?", "ground_truth": ["Proteomics Layer", "Protein Interaction Network"], "concept_id_a": 429, "concept_id_b": 251, "hop_depth": 1}
+{"id": "bioinformatics_T5_149_147", "domain": "bioinformatics", "type": "T5_cross_concept", "query": "How does Local Alignment relate to Pairwise Alignment?", "ground_truth": ["Local Alignment", "Pairwise Alignment"], "concept_id_a": 149, "concept_id_b": 147, "hop_depth": 1}