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May, 2023
了解乳腺癌生存:使用因果关系和语言模型分析多组学数据
Understanding Breast Cancer Survival: Using Causality and Language Models on Multi-omics Data
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Mugariya Farooq, Shahad Hardan, Aigerim Zhumbhayeva, Yujia Zheng, Preslav Nakov...
TL;DR
本文利用因果发现算法和大型语言模型通过对 705 名乳腺癌患者的基因组信息的剖析,从多个角度研究患者存活状况的因素,表明因果发现算法和语言模型的可靠性,有助于深入挖掘临床应用上的可靠因果关系。
Abstract
The need for more usable and explainable
machine learning
models in healthcare increases the importance of developing and utilizing
causal discovery
algorithms, which aim to discover causal relations by analyzing
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