A recent deluge of publicly available multi-omics data has fueled the development of machine learning methods aimed at investigating important questions in genomics. Although the motivations for these methods vary, a task that is commonly adopted is that of profile prediction, where predictions are made for one or more forms of biochemical activity along the genome, for example, histone modification, chromatin accessibility, or protein binding. In this review, we give an overview of the research works performing profile prediction, define two broad categories of profile prediction tasks, and discuss the types of scientific questions that can be answered in each.

中文翻译：

---

机器学习用于基因组学中的轮廓预测

最近大量公开的多组学数据推动了旨在研究基因组学中重要问题的机器学习方法的发展。尽管这些方法的动机各不相同，但通常采用的任务是轮廓预测，其中预测沿基因组的一种或多种形式的生化活动，例如组蛋白修饰、染色质可及性或蛋白质结合。在这篇综述中，我们概述了执行轮廓预测的研究工作，定义了两大类轮廓预测任务，并讨论了每种类型可以回答的科学问题。