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

Multi-Instance Multi-Label Learning for Gene Mutation Prediction in Hepatocellular Carcinoma
Title:
Multi-Instance Multi-Label Learning for Gene Mutation Prediction in Hepatocellular Carcinoma
Other Titles:
2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)
Keywords:
Publication Date:
27 August 2020
Citation:
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.
Abstract:
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.
License type:
Publisher Copyright
Funding Info:
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
Description:
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ISSN:
1558-4615
ISBN:
978-1-7281-1990-8
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