In the past few decades, we have witnessed tremendous advancements in biology, life sciences and healthcare. These advancements are due in no small part to the big data made available by various high-throughput technologies, the ever-advancing computing power, and the algorithmic advancements in machine learning. Specifically, big data analytics such as statistical and machine learning has become an essential tool in these rapidly developing fields. As a result, the subject has drawn increased attention and many review papers have been published in just the past few years on the subject. Different from all existing reviews, this work focuses on the application of systems, engineering principles and techniques in addressing some of the common challenges in big data analytics for biological, biomedical and healthcare applications. Specifically, this review focuses on the following three key areas in biological big data analytics where systems engineering principles and techniques have been playing important roles: the principle of parsimony in addressing overfitting, the dynamic analysis of biological data, and the role of domain knowledge in biological data analytics.

中文翻译：

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系统工程原理与技术在生物大数据分析中的应用

在过去的几十年中，我们目睹了生物学，生命科学和医疗保健领域的巨大进步。这些进步在很大程度上要归功于各种高通量技术所提供的大数据，不断发展的计算能力以及机器学习中的算法进步。具体而言，诸如统计和机器学习之类的大数据分析已成为这些快速发展领域中的重要工具。结果，该主题引起了越来越多的关注，并且在过去几年中已发表了许多有关该主题的评论论文。与所有现有的评论不同，这项工作着重于系统，工程原理和技术的应用，以应对生物，生物医学和医疗保健应用的大数据分析中的一些常见挑战。特别，