The expanding scale and inherent complexity of biological data have encouraged a growing use of machine learning in biology to build informative and predictive models of the underlying biological processes. All machine learning techniques fit models to data; however, the specific methods are quite varied and can at first glance seem bewildering. In this Review, we aim to provide readers with a gentle introduction to a few key machine learning techniques, including the most recently developed and widely used techniques involving deep neural networks. We describe how different techniques may be suited to specific types of biological data, and also discuss some best practices and points to consider when one is embarking on experiments involving machine learning. Some emerging directions in machine learning methodology are also discussed.

生物数据不断扩大的规模和固有的复杂性促使越来越多地使用机器学习在生物学中建立潜在生物过程的信息和预测模型。所有机器学习技术都使模型适合数据；但是，具体方法千差万别，乍看之下令人眼花缭乱。在这篇评论中，我们旨在向读者简要介绍一些关键的机器学习技术，包括最近开发和广泛使用的涉及深度神经网络的技术。我们描述了不同的技术如何适用于特定类型的生物数据，还讨论了一些最佳实践和在开始涉及机器学习的实验时需要考虑的要点。还讨论了机器学习方法中的一些新兴方向。