论文标题
用于基因突变预测肝细胞癌的多标签学习多标签学习
Multi-Instance Multi-Label Learning for Gene Mutation Prediction in Hepatocellular Carcinoma
论文作者
论文摘要
肝细胞癌（HCC）中的基因突变预测对于个性化治疗和精确医学具有很高的诊断和预后价值。在本文中，我们通过多标签的多标签学习解决了这个问题，以解决标签相关，标签表示等上的困难。此外，还针对数据不平衡采用有效的过度采样策略。实验结果表明了所提出的方法的优越性。
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.
