Xu Kaixin, Zhao Ziyuan, Gu Jiapan, Zeng Zeng, Ying Chan Wan, Choon Lim Kheng, Hua Thng Choon, Chow Pierce Kh
Annu Int Conf IEEE Eng Med Biol Soc. 2020 Jul;2020:6095-6098. doi: 10.1109/EMBC44109.2020.9175293.
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.
肝细胞癌(HCC)中的基因突变预测对于个性化治疗和精准医学具有重要的诊断和预后价值。在本文中,我们采用多实例多标签学习来解决这个问题,以应对标签相关性、标签表示等方面的困难。此外,还应用了一种有效的过采样策略来解决数据不平衡问题。实验结果表明了所提方法的优越性。