Datasets:
license: unknown
task_categories:
- question-answering
tags:
- genomics
- knowledge-graph
- multi-hop-reasoning
- biology
pretty_name: GenBench CoCG QA Dataset
dataset_info:
features:
- name: id
dtype: string
- name: task_type
dtype: string
- name: pipeline
dtype: string
- name: difficulty
dtype: string
- name: num_hops
dtype: int64
- name: question
dtype: string
- name: context
dtype: string
- name: reasoning_chain
struct:
- name: steps
list:
- name: hop
dtype: int64
- name: source_node_id
dtype: string
- name: source_node_type
dtype: string
- name: source_node_label
dtype: string
- name: edge_relation
dtype: string
- name: edge_evidence_type
dtype: string
- name: edge_confidence
dtype: float64
- name: edge_source_db
dtype: string
- name: target_node_id
dtype: string
- name: target_node_type
dtype: string
- name: target_node_label
dtype: string
- name: human_readable
dtype: string
- name: total_hops
dtype: int64
- name: start_node_id
dtype: string
- name: end_node_id
dtype: string
- name: answer
dtype: string
- name: modalities
list: string
- name: modality_data
list:
- name: family
dtype: string
- name: tag
dtype: string
- name: anchor_node_id
dtype: string
- name: source_db
dtype: string
- name: payload
struct:
- name: gene
dtype: string
- name: tissues
list: string
- name: tpm_median
list: float64
- name: n_tissues
dtype: int64
- name: top_tissue
dtype: string
- name: chrom
dtype: string
- name: center
dtype: int64
- name: start
dtype: int64
- name: end
dtype: int64
- name: width
dtype: int64
- name: sequence
dtype: string
- name: biosample
dtype: string
- name: marks
struct:
- name: H3K4me1
dtype: float64
- name: H3K4me3
dtype: float64
- name: H3K27ac
dtype: float64
- name: state
dtype: string
- name: hops
list: int64
- name: evidence
list:
- name: source_db
dtype: string
- name: source_id
dtype: string
- name: text
dtype: string
- name: confidence
dtype: float64
- name: metadata
struct:
- name: gene_a
dtype: string
- name: gene_b
dtype: string
- name: path_length
dtype: int64
- name: gene
dtype: string
- name: tissue
dtype: string
- name: element_type
dtype: string
- name: variant
dtype: string
- name: pathway
dtype: string
- name: mode
dtype: string
- name: fabricated
dtype: string
- name: real_options
list: string
- name: protein
dtype: string
- name: threshold
dtype: int64
- name: partner_count
dtype: int64
- name: disease
dtype: string
- name: pathogenic_variants
list: string
- name: top_tissue
dtype: string
- name: top_tpm
dtype: float64
- name: path_confidence_score
dtype: float64
splits:
- name: train
num_bytes: 19898486
num_examples: 4900
download_size: 17322981
dataset_size: 19898486
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
GenBench CoCG QA Dataset
Multi-hop genetic reasoning QA items generated from GenBench's knowledge graph (Ensembl, ClinVar, VEP, BioGRID, STRING, Reactome, UniProt, GO, SIGNOR, OmniPath, KEGG, DisGeNET, OpenTargets, PubTator3, GTEx, and more), built for CoCG (Co-Evolving Confidence Graph) agent training.
1464 items across 6 task types.
Task types
| task_type | count |
|---|---|
disease_reasoning |
244 |
hallucination_detection |
244 |
interaction_propagation |
244 |
mechanistic_explanation |
244 |
path_traversal |
244 |
regulatory_reasoning |
244 |
Schema
Each item has:
id,task_type,pipeline(coding_variant/noncoding_regulatory),difficultyquestion,answer,choices(MCQ options, when applicable)context-- either a templated chain narration, or (ifllm_rewritewas applied) an LLM-rewritten fluent Step/Evidence/Interpretation/Conclusion narrativereasoning_chain-- the grounded, machine-checkable multi-hop path (steps: each withsource_node_id/target_node_id/edge_relation/edge_confidence/edge_source_db), never touched by any LLM stepmodality_data-- raw modality payloads (sequence, structural, transcriptomic, post_translational, signaling_role, etc.) attached to the chain's anchor nodesevidence-- supporting evidence entries with source database/PMIDpath_confidence_score-- continuous, confidence-derived difficulty score
Companion graph
graph.json (if included in this repo) is the exact knowledge graph these
items' reasoning_chain node IDs refer to -- load it with
GenBench's GraphBuilder.load() to
resolve full node/edge attributes beyond what's inlined in each item.
Source
Generated from data\processed\noncoding_qa_dataset.jsonl in GenBench,
the substrate for CoCG (Co-Evolving Confidence Graph) agent training --
per-edge, per-modality KG confidence that co-adapts with an RL policy
during training rather than treating the KG as a frozen oracle.