学习基因组表达以预测癌症的临床结局
Learning Genomic Representations to Predict Clinical Outcomes in Cancer
摘要：
基因组学正在迅速改变医学实践和基础生物医学研究，提供对疾病机制的见解并改进治疗策略，特别是在癌症方面。从高维基因组分析预测患者疾病未来进程的能力对于实现基因组医学的前景至关重要，但对最先进的生存分析方法提出了重大挑战。在这篇摘要中，我们提出了一个研究，在学习基因组表示与神经网络预测癌症患者的生存。我们证明了这种方法的优势，现有的生存分析方法使用脑肿瘤数据。
英文摘要：
Genomics are rapidly transforming medical practice and basic biomedical research, providing insights into disease mechanisms and improving therapeutic strategies, particularly in cancer. The ability to predict the future course of a patient's disease from high-dimensional genomic profiling will be essential in realizing the promise of genomic medicine, but presents significant challenges for state-of-the-art survival analysis methods. In this abstract we present an investigation in learning genomic representations with neural networks to predict patient survival in cancer. We demonstrate the advantages of this approach over existing survival analysis methods using brain tumor data.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
