Albaradei Somayah, Thafar Maha, Alsaedi Asim, Van Neste Christophe, Gojobori Takashi, Essack Magbubah, Gao Xin
Computational Bioscience Research Center (CBRC), Computer, Electrical and Mathematical Sciences and Engineering Division (CEMSE), King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Saudi Arabia.
King Abdulaziz University, Faculty of Computing and Information Technology, Jeddah, Saudi Arabia.
Comput Struct Biotechnol J. 2021 Sep 4;19:5008-5018. doi: 10.1016/j.csbj.2021.09.001. eCollection 2021.
Knowing metastasis is the primary cause of cancer-related deaths, incentivized research directed towards unraveling the complex cellular processes that drive the metastasis. Advancement in technology and specifically the advent of high-throughput sequencing provides knowledge of such processes. This knowledge led to the development of therapeutic and clinical applications, and is now being used to predict the onset of metastasis to improve diagnostics and disease therapies. In this regard, predicting metastasis onset has also been explored using artificial intelligence approaches that are machine learning, and more recently, deep learning-based. This review summarizes the different machine learning and deep learning-based metastasis prediction methods developed to date. We also detail the different types of molecular data used to build the models and the critical signatures derived from the different methods. We further highlight the challenges associated with using machine learning and deep learning methods, and provide suggestions to improve the predictive performance of such methods.
由于知道转移是癌症相关死亡的主要原因，因此激励了旨在揭示驱动转移的复杂细胞过程的研究。技术的进步，特别是高通量测序的出现，提供了有关这些过程的知识。这些知识推动了治疗和临床应用的发展，现在正被用于预测转移的发生，以改善诊断和疾病治疗。在这方面，也已经探索了使用基于机器学习以及最近基于深度学习的人工智能方法来预测转移的发生。这篇综述总结了迄今为止开发的不同的基于机器学习和深度学习的转移预测方法。我们还详细介绍了用于构建模型的不同类型的分子数据以及从不同方法中得出的关键特征。我们进一步强调了使用机器学习和深度学习方法所面临的挑战，并提供了提高这些方法预测性能的建议。
