神经网络自编码器算法在癌症信息学研究中的应用

癌症已经被广泛认为是高度异质性的疾病,癌症的早期诊断、分型和预后已成为癌症研究的关注重点。在大数据时代,对海量癌症生物医学数据进行高效的数据挖掘是生物信息学面临的重要挑战。自编码器(Autoencoder)作为神经网络的一种典型模型,能够通过无监督的方式高效地学习输入数据的特征,进而对生物数据进行整合与挖掘。文中首先介绍了自编码器模型结构并阐述其工作流程,之后结合多种类型的生物医学数据总结自编码器在癌症信息学研究领域的进展,并展望其发展趋势及应用方向。

Application of neural network autoencoder algorithm in the cancer informatics research

Cancers have been widely recognized as highly heterogeneous diseases, and early diagnosis and prognosis of cancer types have become the focus of cancer research. In the era of big data, efficient mining of massive biomedical data has become a grand challenge for bioinformatics research. As a typical neural network model, the autoencoder is able to efficiently learn the features of input data by unsupervised training method and further help integrate and mine the biological data. In this article, the primary structure and workflow of the autoencoder model are introduced, followed by summarizing the advances of the autoencoder model in cancer informatics using various types of biomedical data. Finally, the challenges and perspectives of the autoencoder model are discussed.