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arXiv 2005.040732020-05-08cs.LGq-bio.GNstat.ML

Multi-Instance Multi-Label Learning for Gene Mutation Prediction in Hepatocellular Carcinoma

Institute for Infocomm Research (I2R), Agency for Science, Technology and Research (A*STAR)（新加坡科技研究局资讯通信研究院）；National University of Singapore（新加坡国立大学）；National Cancer Centre（新加坡国家癌症中心）；Singapore General Hospital（新加坡中央医院）

Kaixin Xu, Ziyuan Zhao, Jiapan Gu, Zeng Zeng, Chan Wan Ying, Lim Kheng Choon, Thng Choon Hua, Pierce KH Chow

备注：: Accepted version to be published in the 42nd IEEE Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2020, Montreal, Canada
发表信息：: 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)
DOI：: 10.1109/EMBC44109.2020.9175293

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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.

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