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Updated: Jun 27, 2025

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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用跨模态变异自编码器 (DACMVA) 进行数据增强,用于癌症生存预测

Sara Rajaram1, Cassie S Mitchell1,2

  • 1Laboratory for Pathology Dynamics, Georgia Institute of Technology and Emory University, Atlanta, GA 30332, USA.

Information (Basel)
|April 26, 2024
PubMed
概括

DACMVA通过增强跨模式数据和赋值缺失值来增强预测医学的深度学习. 这种新的框架改善了癌症生存预测,即使基因表达数据有限或缺失.

关键词:
生成性的对抗网络.变量自动编码器 变量自动编码器癌症生存率预测 癌症生存率预测数据增强数据增强

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