Bayesian Classifier for DLBCL Survival Forecasting

The patent extracts only the necessary RNA-based biomarkers from the complete genomic information obtained through whole exome sequencing. By focusing on specific RNA markers that are most relevant for DLBCL subclassification and survival prediction, the method achieves comprehensive classification without requiring the resource-intensive whole exome sequencing process, thus improving clinical practicality while maintaining classification completeness.

The patent employs RNA-based biomarkers that can be obtained through more accessible and less expensive targeted RNA sequencing methods compared to whole exome sequencing. These RNA markers serve as surrogate indicators that provide the necessary classification information without requiring the expensive and complex genomic sequencing infrastructure, making the classification system more clinically practical.

The patent performs preliminary selection and validation of RNA-based biomarkers using machine learning algorithms on training datasets before clinical application. By pre-training the classification model with known survival outcomes and validating its predictive accuracy, the system establishes a robust framework that improves survival prediction accuracy while containing method complexity through systematic preprocessing and model validation steps.

The machine learning algorithm automatically identifies and weights the most predictive RNA biomarkers from the sequencing data without requiring manual interpretation or complex manual analysis. The system self-optimizes by learning from training data which biomarkers and combinations provide the best survival prediction, thereby improving accuracy while the automated nature of the algorithm helps manage method complexity through computational rather than manual processes.