Machine learning-enhanced discovery of tsRNA-mRNA regulatory networks: identifying novel diagnostic biomarkers and therapeutic targets in breast cancer.

{"sci_version": "1.0", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:40746721", "kind": "science-record", "revision": 1, "content_hash": "sha256:13e9215edc6bf2bc97690329c0abd604b8713f6821ad302e449b3c6a5d2f97bc", "created_at": "2026-11-01T10:25:52Z", "updated_at": "2026-11-01T10:25:52Z", "generator": {"name": "corpus-assay-engine", "converter": "sep_to_sci_v1", "source_package_schema": "urn:bntrg:sep:1.0.0"}, "paper": {"state": "partial", "title": "Machine learning-enhanced discovery of tsRNA-mRNA regulatory networks: identifying novel diagnostic biomarkers and therapeutic targets in breast cancer.", "doi": "10.3389/fphar.2025.1640192", "pmid": "40746721", "authors": null, "journal": "Frontiers in pharmacology", "year": 2025, "abstract": null, "claims": [{"id": "claim-1", "statement": "the headline numerical result the paper states for its main finding", "kind": "quantitative", "state": "complete", "target": {"value": 0.98, "unit": "auc", "tolerance": null, 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"gaps": ["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:40746721' does not resolve to a declared asset"]}, "data": {"state": "partial", "assets": [{"id": "source:pmid:40746721", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.3389/fphar.2025.1640192", "sha256": "sha256:82aa03dcc4f36ab4475e71e4eb195be0e5947f7f673c0975fd671a0b74a619df", "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.3389/fphar.2025.1640192", "version": null, "note": "Source read by the package builder: full_text.", "structure": {"access_state": "full_text"}}], "gaps": ["asset:source:pmid:40746721 not verified"]}, "analysis": {"state": "absent", "methods": [], "targets": [], "gaps": ["method", "targets"]}, 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"prompt_version": "full-text-reading-v1", "prompt_hash": "73203f22639d14c1f095d0f0933119d87c24b34100fb61149e8695a1af0bdb1f", "source_hash": "82aa03dcc4f36ab4475e71e4eb195be0e5947f7f673c0975fd671a0b74a619df", "raw_response_sha256": "sha256:9572f83d08f427bb57d74685c0c0f2e688c6836eb5b0a23ea0ec9fdea6ec7d00", "raw_response_chars": 49246}], "target_providers": [{"provider": "minimax", "model": "MiniMax-M3.1-Flash", "prompt_version": "replication-target-v2", "prompt_hash": "e0c4eaaffcb8a2dbad3933943dd6e77f843b2a9d9d406f24ff935213c65d3d68", "source_hash": "82aa03dcc4f36ab4475e71e4eb195be0e5947f7f673c0975fd671a0b74a619df", "raw_response": "{\"parent_value\":0.98,\"unit\":\"AUC\",\"evidence\":\"achieving AUC of 0.98 in discovery and 0.82 in validation cohorts.\",\"target_result\":\"0.98\",\"number_stated\":true}", "reading": {"parent_value": 0.98, "unit": "AUC", "evidence": "achieving AUC of 0.98 in discovery and 0.82 in validation cohorts.", "target_result": "0.98", "number_stated": true, "source_locator": "chars 1305-1371 of source sha256 82aa03dcc4f36ab4475e71e4eb195be0e5947f7f673c0975fd671a0b74a619df"}, "recorded_at": "2026-10-31T15:24:35.529110+00:00"}, {"provider": "minimax_alt", "model": "MiniMax-M3", "prompt_version": "replication-target-v2", "prompt_hash": "c4fb1dd6fceee421e4169df3e01c3ccae7e83dc07fa061a371b2f4348f45f983", "source_hash": "82aa03dcc4f36ab4475e71e4eb195be0e5947f7f673c0975fd671a0b74a619df", "raw_response": "<think>We need answer JSON only. Need identify single numerical result replication registered target headline numerical result paper states main finding. Source abstract/results: four-tsRNA signature achieving AUC 0.98 discovery and 0.82 validation. Registered target vaguely \"the headline numerical result the paper states for its main finding\". Need choose headline number. Likely AUC 0.98, unit AUC. Need exact contiguous quotation containing number and attribution whole clause. Evidence exact quote perhaps \"We identified 297 differentially expressed tsRNAs and developed a four-tsRNA signature (...) achieving AUC of 0.98 in discovery and 0.82 in validation cohorts.\" But asks single numerical result and headline number only, value paper states for registered target. Is target perhaps main finding is diagnostic signature performance. Which one? Headline AUC likely 0.98 discovery. Yet there are two AUC values. They ask \"single numerical result\" perhaps headline number only likely AUC 0.98. Unit must printed: \"AUC\" (dimensionless, paper prints AUC). Evidence exact clause. Need target_result perhaps brief statement, maybe \"The four-tsRNA diagnostic signature achieved an AUC of 0.98 in the discovery cohort.\" Ensure no paraphrase in evidence. Could quote full sentence. parent_value 0.98, unit \"AUC\". number_stated true.\n\nBut think deeply: registered target \"the headline numerical result the paper states for its main finding\". Main finding perhaps four-tsRNA signature, headline numerical result AUC 0.98 in discovery and 0.82 validation. User says single numerical result, headline number only. There is ambiguity which AUC. Maybe headline is discovery AUC 0.98. The wording \"value the paper itself states for the registered target\" likely benchmark setup expects one parent value. The source's abstract main result includes AUC 0.98 discovery and 0.82 validation. Which registered target? Registered target itself generic. Perhaps target asks \"headline numerical result the paper states for its main finding\" means identify primary number, likely 0.98. Need not output multiple. Could evidence quote complete clause including both, but parent_value one. Exact quote includes number. They might want whole clause because attribution may be carried by wording. Quote sentence is safe.\n\nNeed valid JSON no markdown. target_result string. Unit \"AUC\" rather than \"AUC (area under curve)\" because paper prints AUC. Could unit perhaps empty because AUC dimensionless? User says unit must be unit paper prints. It prints \"AUC of 0.98\"; so unit \"AUC\". Let's formulate.\n\nEvidence exact contiguous quote from source. Could quote:\n\"We identified 297 differentially expressed tsRNAs and developed a four-tsRNA signature (tRF-21-FSXMSL73E, tRF-20-XSXMSL73, tRF-23-FSXMSL73 30H, tRF-23-YJE76INB0J) achieving AUC of 0.98 in discovery and 0.82 in validation cohorts.\"\nThis contains headline 0.98 and full clause. Is quote \"do not shorten it\" perhaps they mean evidence quote containing number cannot truncate sentence? Exact full sentence ideal. target_result \"The four-tsRNA diagnostic signature achieved AUC of 0.98 in the discovery cohort.\" This is paraphrase okay outside evidence. Could say headline number only in parent_value. Fine.\n\nPotential issue: registered target is perhaps \"headline numerical result\" and both AUC values mean no single numerical result? But likely benchmark expects 0.98. Let's inspect language. They ask find \"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.\" This is generic extraction instruction, target description generic perhaps deliberately asks headline. The abstract says \"develop a four-tsRNA signature ... achieving AUC of 0.98 in discovery and 0.82 in validation cohorts.\" Which is the headline numerical result? Usually headline metric is AUC 0.98 discovery, 0.82 validation. But they demand single. Perhaps \"registered target\" may refer to \"headline numerical result the paper states for its main finding,\" and likely target result selected in source's title/abstract. The main finding is signature, and there are two cohort results. Maybe target could be discovery performance only because headline is 0.98. Evidence could include both, though they requested headline number only perhaps evidence can contain other number because exact clause includes both. Is that violating \"Report headline number only\"? The JSON evidence quote necessarily includes 0.82 if full sentence. They said exact contiguous quotation containing the number; don't shorten it; attribution can be carried by parenthetical list etc. \"When that is how the text links them, quote the whole clause so a reader can check the attribution for themselves.\" Could quote a narrower contiguous exact clause that attributes 0.98, but source has no subordinate clause separately except entire sentence. Yet \"four-tsRNA signature (...) achieving AUC of 0.98 in discovery and 0.82 in validation cohorts.\" includes both. Can't omit. Could perhaps quote \"achieving AUC of 0.98 in discovery\" but not contiguous because source says \"and 0.82...\" after. They insist exact contiguous and do not shorten. So whole sentence with both is right. But report number only in parent_value.\n\nCould evidence use abstract first sentence exactly. Ensure punctuation and Unicode. JSON escapes? Fine.\n\nCould target_result mention both? They say target_result what text states if no number only. Since number stated, likely concise \"The four-tsRNA signature achieved AUC of 0.98 in discovery and 0.82 in validation cohorts.\" But then reports second number in target_result despite headline only? Better \"The four-tsRNA signature achieved an AUC of 0.98 in the discovery cohort.\" Yet this paraphrases. Fine.\n\nNeed perhaps parent_value=0.98, unit=\"AUC\", evidence exact full sentence, target_result \"The four-tsRNA diagnostic signature achieved AUC of 0.98 in the discovery cohort.\", true.\n\nLet's ensure no issue with \"developed a four-tsRNA signature\" also has number four but not numerical result. Main finding could be 297 DE tsRNAs? No, headline result likely diagnostic AUC. The title emphasizes biomarkers and targets. Abstract's most salient performance number is AUC. \"headline numerical result\" likely 0.98. Let's answer JSON only.</think>\n\n{\"parent_value\":0.98,\"unit\":\"AUC\",\"evidence\":\"We identified 297 differentially expressed tsRNAs and developed a four-tsRNA signature (tRF-21-FSXMSL73E, tRF-20-XSXMSL73, tRF-23-FSXMSL730H, tRF-23-YJE76INB0J) achieving AUC of 0.98 in discovery and 0.82 in validation cohorts.\",\"target_result\":\"The four-tsRNA diagnostic signature achieved an AUC of 0.98 in the discovery cohort.\",\"number_stated\":true}", "reading": {"parent_value": 0.98, "unit": "AUC", "evidence": "We identified 297 differentially expressed tsRNAs and developed a four-tsRNA signature (tRF-21-FSXMSL73E, tRF-20-XSXMSL73, tRF-23-FSXMSL730H, tRF-23-YJE76INB0J) achieving AUC of 0.98 in discovery and 0.82 in validation cohorts.", "target_result": "The four-tsRNA diagnostic signature achieved an AUC of 0.98 in the discovery cohort.", "number_stated": true, "source_locator": "chars 1144-1371 of source sha256 82aa03dcc4f36ab4475e71e4eb195be0e5947f7f673c0975fd671a0b74a619df"}, "recorded_at": "2026-10-31T15:24:43.800186+00:00"}], "agreement": true, "disagreements": {"target_result": {"minimax": "A four-tsRNA signature (tRF-21-FSXMSL73E, tRF-20-XSXMSL73, tRF-23-FSXMSL730H, tRF-23-YJE76INB0J) achieved AUC of 0.98 in the discovery cohort and 0.82 in the validation cohort.", "minimax_alt": "We identified 297 differentially expressed tsRNAs and developed a four-tsRNA signature (tRF-21-FSXMSL73E, tRF-20-XSXMSL73, tRF-23-FSXMSL730H, tRF-23-YJE76INB0J) achieving AUC of 0.98 in discovery and 0.82 in validation cohorts.", "decisive": false}, "analysis_methods": {"minimax": ["Random forest algorithm", "DESeq2", "Wald test", "Benjamini-Hochberg false discovery rate control", "Unsupervised hierarchical clustering with Euclidean distance and complete linkage", "Wilcoxon rank-sum test", "ComBat batch-effect correction", "Kruskal-Wallis test followed by Dunn’s post hoc test", "Kaplan-Meier method", "Log-rank test", "Cox proportional hazards regression", "CIBERSORT", "Spearman’s correlation", "ESTIMATE algorithm", "Pearson correlation analysis", "GSVA", "GSEA", "WGCNA", "Student’s t-test", "One-way ANOVA"], "minimax_alt": ["Custom bioinformatics pipeline for tsRNA profile extraction", "DESeq2 differential expression analysis with median-of-ratios normalization and the Wald test", "Benjamini-Hochberg false discovery rate control", "Random forest algorithm for diagnostic model development", "Correlation analysis and RNAhybrid for tsRNA-mRNA regulatory relationship identification", "Wilcoxon rank-sum test and ComBat batch-effect correction for pan-cancer expression analysis", "Student's t-test, one-way ANOVA, Kruskal-Wallis test, and Dunn's post hoc test", "Kaplan-Meier method, log-rank test, and Cox proportional hazards regression", "CIBERSORT and ESTIMATE algorithms with Spearman's rank correlation for immune infiltration analysis", "Pearson correlation for drug sensitivity analysis", "Gene Set Variation Analysis (GSVA), Gene Set Enrichment Analysis (GSEA), and Weighted Gene Co-expression Network Analysis (WGCNA)"], "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:a79a77a0911a76d0697aa77d6a14eb3f32509e6dd31de73a87bff25b1888f78b", "type": "sep_package", "label": "SEP v1 package for pmid:40746721"}], "activities": [{"id": "activity:conversion:pmid:40746721", "type": "conversion", "used": ["package:a79a77a0911a76d0697aa77d6a14eb3f32509e6dd31de73a87bff25b1888f78b"], "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": "4c3857ac-5f1b-49ac-ac16-0d757dc207db", "parent_hash": "a79a77a0911a76d0697aa77d6a14eb3f32509e6dd31de73a87bff25b1888f78b", "fork_of": null, "generation": 1}, "gaps": ["abstract", "asset:source:pmid:40746721 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:40746721' does not resolve to a declared asset", "targets"]}