Sherbet Gajanan V, Woo Wai Lok, Dlay Satnam
The Institute for Molecular Medicine, Huntington Beach, CA, U.S.A.
School of Engineering, University of Newcastle upon Tyne, Newcastle upon Tyne, U.K.
Anticancer Res. 2018 Dec;38(12):6607-6613. doi: 10.21873/anticanres.13027.
Artificial intelligence was recognised many years ago as a potential and powerful tool to predict disease outcome in many clinical situations. The conventional approaches using statistical methods have provided much information, but are subject to limitations imposed by the complexity of medical data. The structures of the important variants of the machine learning system artificial neural networks (ANN) are discussed and emphasis is given to the powerful analytical support that could be provided by ANN for the prediction of cancer progression and prognosis. The predictive ability of the cellular markers, DNA ploidy and cell-cycle profiles, and molecular markers, such as tumour promoter and suppressor gene, and growth factor and steroid hormone receptors in breast cancer management were also analysed. ANN systems have been successfully deployed to evaluate microRNA profiles of tumours which saliently sway cancer progression and prognosis of the disease, thus counteracting the negative implications of their numerical abundance. Finally, in this setting, the prospective technical improvements in artificial neural networks, as hybrid systems in combination with fuzzy logic and artificial immune networks were also addressed.
许多年前，人工智能就被视为一种在多种临床情况下预测疾病结果的潜在且强大的工具。使用统计方法的传统方法已经提供了很多信息，但受到医学数据复杂性所带来的限制。本文讨论了机器学习系统人工神经网络（ANN）重要变体的结构，并强调了ANN可为癌症进展和预后预测提供的强大分析支持。还分析了细胞标志物、DNA倍性和细胞周期谱以及分子标志物（如肿瘤启动子和抑制基因、生长因子和类固醇激素受体）在乳腺癌管理中的预测能力。ANN系统已成功用于评估肿瘤的微小RNA谱，这些谱显著影响癌症的进展和疾病预后，从而抵消了其数量众多所带来的负面影响。最后，在这种背景下，还探讨了人工神经网络作为与模糊逻辑和人工免疫网络相结合的混合系统的前瞻性技术改进。
