Machine learning-based identification of telomere-related gene signatures for prognosis and immunotherapy response in hepatocellular carcinoma.

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"analysis": {"state": "absent", "methods": [], "targets": [], "gaps": ["method", "targets"]}, "capability": {"state": "absent", "requirements": [], "gaps": ["capability"]}, "readiness": {"state": "partial", "tier": null, "blocking_axis": null, "blocked_on": null, "axes": {}, "can_run": {"dsl_operations": [], "assets": ["source:pmid:40102883", "asset:table:pmid:40102883:1"], "steps": ["read_published_estimate"]}, "must_build": {"dsl_operations": [], "converters": [], "readers": ["xml"], "capabilities": [], "data_gaps": ["source:pmid:40102883"]}, "gaps": ["corpus_record_absent"]}, "review": {"state": "partial", "machine_critique": {"status": "passed", "providers": [{"provider": "minimax", "model": "MiniMax-M3.1-Flash", "prompt_version": "full-text-reading-v1", "prompt_hash": "a4189cf7286b75323cb8f1e6b5f1e1565bd9a0d2e828825afe8230cd955ae6b8", "source_hash": "1ec8037ee004da2bd2e514f5d3bbeaafcd576a7bf31709c44c2bdb731f233078", "raw_response_sha256": "sha256:338ed1cf7ffbb0d9eda4049cfa332093efdc85a315e6478be716fc039f7945db", "raw_response_chars": 3828}, {"provider": "minimax_alt", "model": "MiniMax-M3", "prompt_version": "full-text-reading-v1", "prompt_hash": "07986b6ddd1db002b25f478c8bc9d1eb3f06a701ede31f25e43ea6b7c0be524c", "source_hash": "1ec8037ee004da2bd2e514f5d3bbeaafcd576a7bf31709c44c2bdb731f233078", "raw_response_sha256": "sha256:1eed1b26d14e1e8693a772b89a4abde88bb3eb57507798bb02262dee8ea5d450", "raw_response_chars": 36868}], "target_providers": [{"provider": "minimax", "model": "MiniMax-M3.1-Flash", "prompt_version": "replication-target-v2", "prompt_hash": "f750d98b27fead87f549f4b70805fd68f800a6977a37aa895e89d681db898e2f", "source_hash": "1ec8037ee004da2bd2e514f5d3bbeaafcd576a7bf31709c44c2bdb731f233078", "raw_response": "{\"parent_value\":6,\"unit\":\"TRGs\",\"evidence\":\"Finally, six TRGs (CDC20, TRIP13, EZH2, AKR1B10, ESR1, and DNAJC6) were identified to formulate the risk score (RS) model, which independently predicted prognosis for HCC.\",\"target_result\":\"Six telomere-related genes were identified to formulate the risk score model.\",\"number_stated\":true}", "reading": {"parent_value": 6, "unit": "TRGs", "evidence": "Finally, six TRGs (CDC20, TRIP13, EZH2, AKR1B10, ESR1, and DNAJC6) were identified to formulate the risk score (RS) model, which independently predicted prognosis for HCC.", "target_result": "Six telomere-related genes were identified to formulate the risk score model.", "number_stated": true, "source_locator": "chars 1249-1420 of source sha256 1ec8037ee004da2bd2e514f5d3bbeaafcd576a7bf31709c44c2bdb731f233078"}, "recorded_at": "2026-10-31T15:34:29.098578+00:00"}, {"provider": "minimax_alt", "model": "MiniMax-M3", "prompt_version": "replication-target-v2", "prompt_hash": "6fb6b5512dbeb262445f7df98fb5c2c1dc8eb96156c6514688f2ca1d3400c58e", "source_hash": "1ec8037ee004da2bd2e514f5d3bbeaafcd576a7bf31709c44c2bdb731f233078", "raw_response": "<think>We need answer JSON only. Need identify single numerical result headline main finding registered target. Source is abstract plus excerpts. Need infer headline numerical result paper states main finding. Likely six TRGs? But asks headline number, value paper itself states for registered target, unit. \"Finally, six TRGs ... identified to formulate risk score model...\" Is that headline numerical result? Could be perhaps model AUC not included in supplied text. We extract only supplied. Registered target broad: \"headline numerical result the paper states for its main finding.\" Need find number attached to main finding. Abstract includes 25 TRGs from overlap 34, finally six TRGs formulate model. Which single numerical result headline? Could be six TRGs. Unit likely \"TRGs\" or maybe unit empty? Requirement unit paper prints. \"six TRGs (CDC20...)\" Number spelled word \"six\", but parent_value numerical 6 and evidence exact quote. Yet they ask numerical result, likely count of six genes. Need output evidence contiguous exact quote containing number. It says \"Finally, six TRGs...\" That's exact.\n\nCould headline numerical result be 34 TRGs? Main finding could risk model based on six TRGs. Likely six. They explicitly say single numerical result, perhaps benchmark expects headline statistic like AUC, HR, p. But supplied excerpt omitted results specifics. Need carefully parse user wording: \"Read supplied paper text and find the single numerical result that a replication of the registered target has to reproduce. Report the headline number only: the value the paper itself states for the registered target, not a value you calculated, converted, averaged or reconstructed. ... REGISTERED TARGET: the headline numerical result the paper states for its main finding\". They likely expect extraction from text. Here abstract's main numerical finding is six TRGs. Maybe \"25 TRGs from overlapping set of 34 genes\" and \"Finally, six TRGs ...\". Headline number likely six.\n\nNeed target_result perhaps \"The final risk-score model was formulated from six telomere-related genes.\" number_stated true, parent_value 6, unit \"TRGs\", evidence exact whole clause. Ensure exact source text and no altered punctuation. Could quote: \"Finally, six TRGs (CDC20, TRIP13, EZH2, AKR1B10, ESR1, and DNAJC6) were identified to formulate the risk score (RS) model, which independently predicted prognosis for HCC.\" This is exact contiguous and contains number and target. Unit \"TRGs\". target_result perhaps same factual concise. JSON valid. No extra.</think>\n\n{\"parent_value\":6,\"unit\":\"TRGs\",\"evidence\":\"Finally, six TRGs (CDC20, TRIP13, EZH2, AKR1B10, ESR1, and DNAJC6) were identified to formulate the risk score (RS) model, which independently predicted prognosis for HCC.\",\"target_result\":\"The risk score model was formulated from six telomere-related genes.\",\"number_stated\":true}", "reading": {"parent_value": 6, "unit": "TRGs", "evidence": "Finally, six TRGs (CDC20, TRIP13, EZH2, AKR1B10, ESR1, and DNAJC6) were identified to formulate the risk score (RS) model, which independently predicted prognosis for HCC.", "target_result": "The risk score model was formulated from six telomere-related genes.", "number_stated": true, "source_locator": "chars 1249-1420 of source sha256 1ec8037ee004da2bd2e514f5d3bbeaafcd576a7bf31709c44c2bdb731f233078"}, "recorded_at": "2026-10-31T15:34:30.928989+00:00"}], "agreement": true, "disagreements": {"target_result": {"minimax": "Six TRGs—CDC20, TRIP13, EZH2, AKR1B10, ESR1, and DNAJC6—were identified to formulate a risk score model that independently predicted prognosis and immune response in hepatocellular carcinoma.", "minimax_alt": "Finally, six TRGs (CDC20, TRIP13, EZH2, AKR1B10, ESR1, and DNAJC6) were identified to formulate the risk score (RS) model, which independently predicted prognosis for HCC.", "decisive": false}, "analysis_methods": {"minimax": ["Differential expression analysis", "Cox regression analysis", "Least Absolute Shrinkage and Selection Operator (LASSO) regression", "Gene Ontology (GO) analysis", "Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis", "CIBERSORT analysis", "Correlation analysis", "oncoPredict"], "minimax_alt": ["Differential expression analysis", "Intersection analysis of altered-expression genes with telomere-related genes", "Cox regression analysis", "Least Absolute Shrinkage and Selection Operator (LASSO) regression", "GO and KEGG analysis", "CIBERSORT analysis", "Immune checkpoint correlation analysis", "oncoPredict drug sensitivity analysis"], "decisive": false}}, "agreement_basis": {"policy": "decisive_fields", "decisive_fields": ["work_type", "number_stated", "parent_value", "unit"]}, "blinded": true}, "human_critique": {"status": "not_run", "reviewer": null, "signed_at": null, "conflict_checked": false, "commentary": null}, "stages": [{"key": "target_materials", "status": "pending", "reason": null, "evidence": null}, {"key": "independent_readings", "status": "passed", "reason": "The independent readings are incomplete or disagree; the source evidence must resolve the difference.", "evidence": {"provider_errors": []}}, {"key": "source_resolution", "status": "pending", "reason": null, "evidence": null}, {"key": "protocol_reconstruction", "status": "pending", "reason": null, "evidence": null}, {"key": "control_execution", "status": "pending", "reason": null, "evidence": null}, {"key": "control_comparison", "status": "pending", "reason": null, "evidence": null}, {"key": "publication", "status": "pending", "reason": null, "evidence": null}], "gaps": ["review.human_critique", "review.machine_critique.passed"]}, "execution": {"state": "absent", "status": "not_run", "mode": null, "runs": [], "gaps": ["execution.not_run"]}, "provenance": {"entities": [{"id": "package:51406b0a36bff84e615ffe07cf202b29393cb5c8da4a7e2e640a906ef46c6c6d", "type": "sep_package", "label": "SEP v1 package for pmid:40102883"}], "activities": [{"id": "activity:conversion:pmid:40102883", "type": "conversion", "used": ["package:51406b0a36bff84e615ffe07cf202b29393cb5c8da4a7e2e640a906ef46c6c6d"], "agent": "agent:corpus-assay-engine"}], "agents": [{"id": "agent:minimax", "type": "model", "label": "minimax"}, {"id": "agent:minimax_alt", "type": "model", "label": "minimax_alt"}, {"id": "agent:corpus-assay-engine", "type": "software", "label": "Corpus assay engine"}], "source_run_id": "run-20261023T122449232913", "dsl_catalogue_fingerprint": "343fb30756ba6ec3", "capability_table_fingerprint": "2cb3e42f047d2ecf", "snapshot_id": "ba34b49490d9bfaf"}, "lineage": {"parent_id": "pmid:40102883", "parent_hash": "sha256:3ecd00471f670b124c23a3c8552cd76d031ffde88e850f0a72ed53cb1bf981fd", "fork_of": null, "generation": 2}, "gaps": ["abstract", "asset:source:pmid:40102883 not verified", "authors", "capability", "corpus_record_absent", "execution.not_run", "method", "review.human_critique", "review.machine_critique.passed", "step:obtain_source_data blocked on data:not_downloaded", "step:run_analysis blocked on no owned computational capability is bound to this experiment", "step:run_analysis input 'materials:the source data behind the published result in pmid:40102883' does not resolve to a declared asset", "targets"]}