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CPSM: An R Package for Cancer Patient Survival Risk Model Using Transcriptomics and Clinical Data
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DOI：10.1093/gigascience/giag067.png)
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Abstract
En 中文
Traditional Kaplan-Meier curves capture aggregate survival trends within broad patient subgroups but overlook the heterogeneity of individual patients. In contrast, single-patient survival risk models bridge this gap by incorporating each patient’s unique clinical, genomic, and demographic characteristics, generating personalized survival curves. These individualized visualizations enhance patient-clinician communication by translating complex statistics into intuitive, time-based visuals that are easier to interpret. However, the complexity, high dimensionality, and heterogeneity of multi-omics data present significant challenges for analysis, interpretation, and model development.