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

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

2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)

10.1109/EMBC44109.2020.9175293

https://ieeexplore.ieee.org/document/9175293/authors#authors

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

Tumors

27 August 2020

K. Xu et al., "Multi-Instance Multi-Label Learning for Gene Mutation Prediction in Hepatocellular Carcinoma," 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), 2020, pp. 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.

Publisher Copyright

This research / project is supported by the Singapore - Pre-GAP Grant
Grant Reference no. : Grant No. ACCL/19-GAP023-R20H

This research / project is supported by the Singapore-China - NRF-NSFC Grant
Grant Reference no. : Grant No. NRF2016NRF-NSFC001-111

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https://oar.a-star.edu.sg/communities-collections/articles/16498

1558-4615

978-1-7281-1990-8

Institute for Infocomm Research