  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.
