Multi-task Learning for Cancer Gene Therapy Target Identification

The patent applies preliminary action by using computational methods to pre-screen and prioritize potential therapeutic targets before conducting expensive in-vitro and in-vivo experiments. The system analyzes genomic data, survival data, and literature to generate a ranked list of candidate targets, so that subsequent experimental validation focuses only on the most promising candidates. This preliminary computational filtering step reduces the number of experiments needed while maintaining high confidence in target selection.

The patent replaces mechanical/biological experimental systems with a computational information processing system. Instead of directly conducting time-consuming animal models and clinical experiments to identify targets, the system uses automated data collection, multi-task learning models, and computational analysis to predict therapeutic targets. This substitution of computational mechanics for biological mechanics dramatically improves productivity while maintaining reliability through rigorous statistical modeling.

The system performs preliminary computational analysis to identify and prioritize high-confidence therapeutic targets before committing resources to expensive in-vitro and in-vivo experiments. By pre-filtering candidates using multi-task learning models that integrate genomic, survival, and literature data, the system ensures that expensive experimental validation is performed only on targets with highest predicted efficacy, thereby reducing overall development costs while maintaining reliability.

The patent substitutes computational information processing for costly biological experimentation in the target identification phase. The computational system processes public genomic data, survival data, and literature at minimal cost compared to animal models and clinical experiments. This substitution dramatically reduces development costs for the early-stage target identification while preserving reliability through rigorous statistical modeling and multi-data integration.

The patent merges multiple separate analysis tasks into a unified multi-task learning framework. Instead of running separate analyses for each cancer type, the system simultaneously analyzes multiple cancer types together, sharing computational resources and statistical power. The multi-task learning model learns common patterns across cancer types while also capturing cancer-specific features, thereby maintaining precision for individual cancers while reducing overall system complexity and analysis time.

The patent creates a universal analysis system that handles multiple cancer types through a single multi-task learning model. The model is designed to be multi-functional, capable of analyzing genomic data and survival data across different cancer types simultaneously. This universal approach maintains measurement precision for specific cancer targets by learning cancer-specific patterns, while avoiding the complexity and time consumption of separate dedicated systems for each cancer type.

The patent combines multiple cancer type analyses into a single simultaneous multi-task learning process. By merging the analysis tasks, the system performs what would otherwise require multiple sequential analyses in a single parallel computation. This maintains measurement precision for each cancer type by preserving cancer-specific signal detection, while dramatically reducing total analysis time by eliminating the need to run separate analyses one after another.

The patent enables continuous useful action by running the multi-task learning model simultaneously for all cancer types rather than sequentially. The computational process continuously processes data for multiple cancer types in parallel, maximizing resource utilization and minimizing idle time. This continuous parallel processing maintains precision for each cancer type while reducing total analysis time compared to sequential separate analyses.