Machine learning-based bulk RNA analysis reveals a prognostic signature of 13 cell death patterns and potential therapeutic target of SMAD3 in acute myeloid leukemia.

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"sha256:3f795ef154ce7430db59db1a729d500cd4436fabdcbe626c607a13097da0483e", "raw_response_chars": 5126}, {"provider": "minimax_alt", "model": "MiniMax-M3", "prompt_version": "full-text-reading-v1", "prompt_hash": "b7542e5ba3e983aa304a77a31334d692c5395cfc7bfce0aaa48c15e2cac90ffc", "source_hash": "5267ca5dd4599ec7991507f7cde8591334079cddb8daa242330f39c203de4cd3", "raw_response_sha256": "sha256:1cb59a00371789c70ff4328ecb27068932ac6ad02dc8d7e97b295d95d0322404", "raw_response_chars": 59485}], "target_providers": [{"provider": "minimax", "model": "MiniMax-M3.1-Flash", "prompt_version": "replication-target-v2", "prompt_hash": "2a73c6f1184dcaac11e8f15d45ce1c15ccb338b0e4fa05e23e665c96fdb439aa", "source_hash": "5267ca5dd4599ec7991507f7cde8591334079cddb8daa242330f39c203de4cd3", "raw_response": "{\"parent_value\":13,\"unit\":\"cell death patterns\",\"evidence\":\"Machine learning-based bulk RNA analysis reveals a prognostic signature of 13 cell death patterns and potential therapeutic target of SMAD3 in acute myeloid leukemia\",\"target_result\":\"The paper states a prognostic signature comprising 13 cell death patterns.\",\"number_stated\":true}", "reading": {"parent_value": 13, "unit": "cell death patterns", "evidence": "Machine learning-based bulk RNA analysis reveals a prognostic signature of 13 cell death patterns and potential therapeutic target of SMAD3 in acute myeloid leukemia", "target_result": "The paper states a prognostic signature comprising 13 cell death patterns.", "number_stated": true, "source_locator": "chars 0-165 of source sha256 5267ca5dd4599ec7991507f7cde8591334079cddb8daa242330f39c203de4cd3"}, "recorded_at": "2026-10-31T15:29:46.032578+00:00"}, {"provider": "minimax_alt", "model": "MiniMax-M3", "prompt_version": "replication-target-v2", "prompt_hash": "38612579785e059a6933f43db67249cf3eb43468141a83fa789cd3929d8e0704", "source_hash": "5267ca5dd4599ec7991507f7cde8591334079cddb8daa242330f39c203de4cd3", "raw_response": "<think>We need answer JSON only. Need interpret \"single numerical result that a replication of registered target has to reproduce. headline number only: value paper itself states for registered target, not calculated/converted/averaged/reconstructed. Unit paper prints. Evidence exact contiguous quote containing number. Attribution may via parenthetical list, respectively, or words naming target. Target: headline numerical result paper states for main finding.\"\n\nWe need identify headline numerical result. Source abstract/results. Potential numbers: 13 cell death patterns, 1394 genes, 101 models, ten algorithms, 214 upregulated, 39 prognostic, AUC ≥0.7. Which is \"headline numerical result\" perhaps title says prognostic signature of 13 cell death patterns. Main finding likely \"13 cell death patterns\" (headline numerical result), title exact. They ask \"the headline numerical result the paper itself states for its registered target\" perhaps benchmark extraction task where target broad \"headline numerical result the paper states for its main finding\". Need select single number likely 13 cell death patterns, unit maybe \"cell death patterns\". Need carefully parse.\n\nPaper title: \"Machine learning-based bulk RNA analysis reveals a prognostic signature of 13 cell death patterns and potential therapeutic target of SMAD3 in acute myeloid leukemia\". Headline number likely 13 cell death patterns. But replication must reproduce perhaps main finding \"13 different PCD pathways\" examined. They may expect 13? Let's understand task framing. \"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. The unit must be the unit the paper prints.\" This is likely asking extract \"headline number\" from paper, registered target is description. Could be abstract's key result number, perhaps AUC values ≥ 0.7. We need infer exact intended.\n\nThey explicitly define target as \"the headline numerical result the paper states for its main finding.\" This could be a general extraction where find key quantitative finding, not necessarily title. Candidate:\n- 13 cell death patterns (title, also abstract)\n- 1394 genes representing 13 pathways examined\n- 214 upregulated genes, 39 prognostic\n- 101 models, 10 algorithms\n- AUC ≥0.7\nNo single primary performance metric (C-index values omitted). Which number is \"headline numerical result\"? Title likely gives 13. Since title says \"prognostic signature of 13 cell death patterns\" and target main finding likely this. Unit \"cell death patterns\". Evidence exact contiguous quote containing number and attribution. Could quote title exactly: \"Machine learning-based bulk RNA analysis reveals a prognostic signature of 13 cell death patterns and potential therapeutic target of SMAD3 in acute myeloid leukemia\". parent_value 13, unit \"cell death patterns\". target_result maybe concise \"The prognostic signature comprises 13 cell death patterns.\" Evidence whole title. number_stated true.\n\nNeed ensure \"headline number only\" perhaps they want \"13\" and unit \"PCD pathways.\" They insist unit paper prints. Title prints \"cell death patterns.\" If use abstract: \"A total of 1394 genes representing 13 different PCD pathways were examined...\" But that number is genes examined, not signature number. The title directly attaches 13 to cell death patterns, best evidence.\n\nCould there be a hidden expectation of \"39\" as main finding? Let's investigate. Main study finding is prognostic signature of 13 cell death patterns. The \"headline numerical result\" likely title metric. Prompt perhaps generated around \"registered target\" from some paper replication benchmark, asks identify single numerical result. They intentionally say \"the value the paper itself states for the registered target\" and target description vague. Need determine most salient number.\n\nTitle includes \"13 cell death patterns\". Abstract first line includes 1394 genes representing 13 pathways, then 214, 39, 101, ten, AUC. Among these, title's 13 likely \"headline.\" Word \"headline\" might literally mean in title/headline. Also registered target says main finding; title's main finding exactly \"a prognostic signature of 13 cell death patterns\". So yes.\n\nNeed output valid JSON, no markdown. Parent value number 13. unit perhaps \"cell death patterns\" exactly. evidence exact contiguous source quote. target_result maybe \"The paper states that its prognostic signature comprises 13 cell death patterns.\" Ensure evidence exact quote: source title exact. Could quote full title. They say \"do not shorten it\" likely quote whole clause where target attached, perhaps exact title. Use full title as evidence.\n\nQuestion: \"Report the headline number only\" Could target_result perhaps just number? But schema includes target_result string. We can say \"13 cell death patterns\". Unit \"cell death patterns\". Evidence full title. Is \"pattern\" count considered unit? Yes.</think>\n\n{\"parent_value\":13,\"unit\":\"cell death patterns\",\"evidence\":\"Machine learning-based bulk RNA analysis reveals a prognostic signature of 13 cell death patterns and potential therapeutic target of SMAD3 in acute myeloid leukemia\",\"target_result\":\"The prognostic signature comprises 13 cell death patterns.\",\"number_stated\":true}", "reading": {"parent_value": 13, "unit": "cell death patterns", "evidence": "Machine learning-based bulk RNA analysis reveals a prognostic signature of 13 cell death patterns and potential therapeutic target of SMAD3 in acute myeloid leukemia", "target_result": "The prognostic signature comprises 13 cell death patterns.", "number_stated": true, "source_locator": "chars 0-165 of source sha256 5267ca5dd4599ec7991507f7cde8591334079cddb8daa242330f39c203de4cd3"}, "recorded_at": "2026-10-31T15:29:52.573271+00:00"}], "agreement": false, "disagreements": {"target_result": {"minimax": "The PCD index and prognostic nomogram predicted overall survival in AML with satisfactory predictive power (AUC values ≥ 0.7).", "minimax_alt": "An AML prognostic signature comprising 13 programmed cell death patterns, with a validated nomogram showing AUC values ≥ 0.7 and SMAD3 identified as a potential therapeutic target.", "decisive": false}, "physical_methods": {"minimax": ["In vitro validation of SMAD3 expression", "Treatment with the SMAD3 inhibitor SIS3 as monotherapy and in combination", "Assessment of autophagy-related marker LC3 expression"], "minimax_alt": ["In vitro validation of SMAD3 expression.", "Assessment of AML-cell sensitivity to the SMAD3 inhibitor SIS3.", "Assessment of LC3 expression after SIS3 monotherapy and combined treatment in KMT2A-rearranged cell lines."], "decisive": false}, "analysis_methods": {"minimax": ["Integrated construction of 101 models based on ten machine learning algorithms", "Evaluation of models using the average C-index and number of model genes", "Gene Ontology enrichment analysis", "KEGG enrichment analysis", "Gene set enrichment analysis (GSEA)", "Fisher’s exact test", "Chi-square test", "Wilcoxon rank-sum test", "T test", "Pearson correlation", "Spearman correlation", "Kaplan-Meier analysis", "Cox regression"], "minimax_alt": ["Bulk RNA transcriptome analysis of PCD-related genes.", "Individual prediction of overall survival for upregulated PCD-related genes.", "Integrated machine-learning modelling with 101 models based on ten machine-learning algorithms.", "PCD-index development based on the average C-index and number of model genes identified from machine-learning combinations.", "Prognostic nomogram development and validation using the PCD index, age and ELN risk stratification.", "GO and KEGG enrichment analysis and gene set enrichment analysis.", "Mutation-profile comparison using Fisher’s exact test.", "Kaplan–Meier and Cox regression analyses.", "Pearson and Spearman correlation analyses."], "decisive": false}, "statistical_method": {"minimax": "Kaplan-Meier and Cox regression analyses", "minimax_alt": "Integrated machine-learning modelling consisting of 101 models based on ten machine-learning algorithms.", "decisive": true}}, "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": "blocked", "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.blocked"]}, "execution": {"state": "absent", "status": "not_run", "mode": null, "runs": [], "gaps": ["execution.not_run"]}, "provenance": {"entities": [{"id": "package:d1c5583c73e782e92d55a99772de35feb88dba07139279e8c6a9b5752bb5b8de", "type": "sep_package", "label": "SEP v1 package for pmid:39955536"}], "activities": [{"id": "activity:conversion:pmid:39955536", "type": "conversion", "used": ["package:d1c5583c73e782e92d55a99772de35feb88dba07139279e8c6a9b5752bb5b8de"], "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": "428d16cf-f2e3-4f3e-8319-9c2d58f8f02f", "parent_hash": "d1c5583c73e782e92d55a99772de35feb88dba07139279e8c6a9b5752bb5b8de", "fork_of": null, "generation": 1}, "gaps": ["abstract", "asset:source:pmid:39955536 not verified", "authors", "capability", "corpus_record_absent", "execution.not_run", "method", "review.human_critique", "review.machine_critique.blocked", "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:39955536' does not resolve to a declared asset", "targets"]}