Multi-Instance Multi-Label Learning for Gene Mutation Prediction in Hepatocellular Carcinoma
This work addresses a domain-specific problem for medical diagnostics in precision medicine, but it appears incremental as it applies existing methods to a new dataset.
The paper tackled gene mutation prediction in hepatocellular carcinoma using multi-instance multi-label learning to address label correlations and data imbalance, showing superiority in experimental results.
Gene mutation prediction in hepatocellular carcinoma (HCC) is of great diagnostic and prognostic value for personalized treatments and precision medicine. In this paper, we tackle this problem with multi-instance multi-label learning to address the difficulties on label correlations, label representations, etc. Furthermore, an effective oversampling strategy is applied for data imbalance. Experimental results have shown the superiority of the proposed approach.