What are the Future Directions for Classification Algorithms in Cancer?
The future of classification algorithms in cancer looks promising, with ongoing research focused on addressing current limitations. Areas of interest include:
Explainable AI: Developing models that provide clear explanations for their predictions, improving trust and adoption in clinical settings. Integration of Multi-Omics Data: Combining data from genomics, transcriptomics, proteomics, and more to create comprehensive models of cancer. Transfer Learning: Leveraging knowledge from pre-trained models on similar tasks to improve performance on new tasks with less data. Federated Learning: Enabling collaborative model training across institutions without sharing sensitive patient data.
