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pmid:40177138

Machine learning‑based construction of damage‑associated molecular patterns related score identifies subtypes of pancreatic adenocarcinoma with distinct prognosis.

Oncology letters · 2025 doi:10.3892/ol.2025.14992

Paper

partial

Experiment

partial

Data

partial

Analysis

absent

Execution

absent

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Record details sci_version · id · kind · revision · content_hash · schema · created_at · updated_at · generator
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pmid:40177138
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science-record
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paper object · 10 fields
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title
Machine learning‑based construction of damage‑associated molecular patterns related score identifies subtypes of pancreatic adenocarcinoma with distinct prognosis.
doi
10.3892/ol.2025.14992
pmid
40177138
authors
null
journal
Oncology letters
year
2025
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null
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the headline numerical result the paper states for its main finding
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the headline numerical result the paper states for its main finding
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The number a replication must land on, and the sentence it came from.
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source:pmid:40177138
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the published estimate, its unit and its interval
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The published result cannot be recomputed without the data the paper analysed.
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the source data with its provenance
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no owned computational capability is bound to this experiment
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Re-run the paper's analysis on the source data and produce the registered result. | SEP input not carried into the asset list: materials:the source data behind the published result in pmid:40177138
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minimax
The DAMPscore AUC values for predicting overall survival at 1, 3 and 5 years were 0.65, 0.70 and 0.77 in the Meta-cohort and 0.71, 0.73 and 0.72 in the TCGA-PAAD cohort.
minimax_alt
In the Meta-cohort, the area under the ROC curve (AUC) values for the DAMPscore to predict overall survival at 1, 3 and 5 years were 0.65, 0.70 and 0.77, respectively, while the AUC values for the TCGA-PAAD cohort were 0.71, 0.73 and 0.72, respectively.
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Kaplan-Meier method with the log-rank test
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Pearson correlation
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Wilcox rank-sum test
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minimax
Receiver operating characteristic (ROC) curve analysis using the timeROC package in R
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Receiver operating characteristic (ROC) curve analysis using the timeROC package in R (version 4.1.3)
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target_materials
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Complete file
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