University of Waikato, P.O. Box 1212, Hamilton, New Zealand.
University of Waikato, Hamilton, New Zealand.
Artif Intell Med. 2019 Jun;97:204-214. doi: 10.1016/j.artmed.2019.01.006. Epub 2019 Jan 30.
Neural networks are powerful tools used widely for building cancer prediction models from microarray data. We review the most recently proposed models to highlight the roles of neural networks in predicting cancer from gene expression data. We identified articles published between 2013-2018 in scientific databases using keywords such as cancer classification, cancer analysis, cancer prediction, cancer clustering and microarray data. Analyzing the studies reveals that neural network methods have been either used for filtering (data engineering) the gene expressions in a prior step to prediction; predicting the existence of cancer, cancer type or the survivability risk; or for clustering unlabeled samples. This paper also discusses some practical issues that can be considered when building a neural network-based cancer prediction model. Results indicate that the functionality of the neural network determines its general architecture. However, the decision on the number of hidden layers, neurons, hypermeters and learning algorithm is made using trail-and-error techniques.
神经网络是一种强大的工具，广泛用于从微阵列数据中构建癌症预测模型。我们回顾了最近提出的模型，以强调神经网络在预测基因表达数据中的癌症中的作用。我们使用诸如癌症分类、癌症分析、癌症预测、癌症聚类和微阵列数据等关键字，在科学数据库中确定了 2013 年至 2018 年之间发表的文章。分析这些研究表明，神经网络方法要么用于在预测之前对基因表达进行过滤（数据工程）；要么用于预测癌症的存在、癌症类型或生存风险；要么用于对未标记的样本进行聚类。本文还讨论了在构建基于神经网络的癌症预测模型时可以考虑的一些实际问题。结果表明，神经网络的功能决定了其总体架构。但是，使用试错技术来确定隐藏层、神经元、超参数和学习算法的数量。
