Summary of Mvktrans: Multi-view Knowledge Transfer For Robust Multiomics Classification, by Shan Cong et al.
MVKTrans: Multi-View Knowledge Transfer for Robust Multiomics Classification
by Shan Cong, Zhiling Sang, Hongwei Liu, Haoran Luo, Xin Wang, Hong Liang, Jie Hao, Xiaohui Yao
First submitted to arxiv on: 13 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed MVKTrans framework is a novel approach to address the unique challenges in multiomics prediction. It transfers intra- and inter-omics knowledge in an adaptive manner, suppressing bias transfer and enhancing classification performance. The framework consists of a graph contrastive module that learns underlying patterns in unlabeled data, promoting general and unbiased representations for each modality. Additionally, it features an adaptive cross-omics distillation module that dynamically transfers knowledge from richer to less informative omics, enabling robust integration. Experimental results on four real biomedical datasets demonstrate the superior performance and robustness of MVKTrans compared to state-of-the-art methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Multiomics data is special because it’s hard to understand. This makes it tricky to predict things like what disease someone has or how they’ll respond to treatment. To solve this problem, scientists created a new way to look at multiomics data called MVKTrans. It helps the computer learn from different parts of the data and ignores any biases that might be hiding in there. The new approach also lets it focus on the most important information first, which makes it more accurate. Scientists tested MVKTrans on real medical data and found that it worked better than other methods. |
Keywords
» Artificial intelligence » Classification » Distillation
