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@@ -3,7 +3,8 @@ Codes for "Meta-Path-Based Deep Multiple Instance Learning with Heterogeneous Gr
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# Reference
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If you make advantage of the MilGNet model or its modules proposed in our paper, please cite the following in your manuscript:
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Gu Y, Zheng S, Zhang B, et al. MilGNet: a multi-instance learning-based heterogeneous graph network for drug repositioning[C]//2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2022: 430-437.
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# Overview
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![MilGNet](https://github.com/gu-yaowen/MilGNet/blob/master/model%20structure.png)

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