标题： 深度学习在基于细胞遗传学、年龄和突变预测急性髓系白血病预后的应用 显示英文标题

标题： Application of Deep Learning on Predicting Prognosis of Acute Myeloid Leukemia with Cytogenetics, Age, and Mutations

摘要： 我们探讨如何利用深度学习（DL）来预测急性髓系白血病（AML）的预后。 来自TCGA（癌症基因组图谱）数据库， 本研究使用了94例AML病例。 输入数据包括年龄、10种常见的染色体核型和23种最常见的突变结果；输出是预后 （诊断到死亡，DTD）。 在我们的DL网络中，自编码器被堆叠起来，形成一个分层的DL模型，从中原始数据被压缩和组织，并提取高层次特征。 该网络用R语言编写，设计用于预测给定病例的AML预后（DTD超过或少于730天）。 DL网络在预测预后方面取得了83%的优异准确率。 作为概念验证研究，我们的初步结果展示了DL在未来使用下一代测序（NGS）数据进行预后预测中的实际应用。 显示英文摘要

摘要： We explore how Deep Learning (DL) can be utilized to predict prognosis of acute myeloid leukemia (AML). Out of TCGA (The Cancer Genome Atlas) database, 94 AML cases are used in this study. Input data include age, 10 common cytogenetic and 23 most common mutation results; output is the prognosis (diagnosis to death, DTD). In our DL network, autoencoders are stacked to form a hierarchical DL model from which raw data are compressed and organized and high-level features are extracted. The network is written in R language and is designed to predict prognosis of AML for a given case (DTD of more than or less than 730 days). The DL network achieves an excellent accuracy of 83% in predicting prognosis. As a proof-of-concept study, our preliminary results demonstrate a practical application of DL in future practice of prognostic prediction using next-gen sequencing (NGS) data.