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Updated: Jul 14, 2025

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使用知识蒸和监督变异自编码器集成不完整的多omics数据,用于预测疾病进展

Sima Ranjbari1, Suzan Arslanturk1

  • 1Department of Computer Science, Wayne State University, Detroit, 48202, MI, USA.

Journal of biomedical informatics
|October 9, 2023
PubMed
概括

这项研究介绍了KD-SVAE-VCDN,这是一个整合多omics数据来预测癌症进展的新框架. 该模型准确地预测了乳腺癌和癌患者的生存结果,超过了现有的方法.

关键词:
数据整合数据集成知识的蒸知识的蒸.多模式数据多模式数据预测生存的预测.

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