用深度学习实现生物信息学中的结构化预测
Towards Structured Prediction in Bioinformatics with Deep Learning
摘要：
使用机器学习，尤其是深度学习，来促进生物学研究是一个令人着迷的研究方向。然而，除了标准的分类或回归问题，在生物信息学中，我们经常需要预测更复杂的结构化目标，如2D图像和3D分子结构。上述复杂预测任务被称为结构化预测。结构化预测比传统的分类方法更复杂，但由于生物信息学问题的输出对象复杂，因此结构化预测具有更广泛的应用前景。由于这些结构化预测问题的特性，例如在标签空间内具有特定于问题的约束和依赖性，直接应用现有的深度学习模型可能会导致不满意的结果。在这里，我们认为以下想法可以帮助解决生物信息学中的结构化预测问题。首先，我们可以将深度学习与其他经典算法相结合，例如概率图模型，它明确地对问题结构进行建模。其次，我们可以通过显式或隐式地考虑结构化标签空间和问题约束来设计特定于问题的深度学习架构或方法。我们从四个生物信息学子领域，包括测序分析，结构预测，功能注释和网络分析的六个项目来展示我们的想法。结构化输出涵盖1D信号、2D图像、3D结构、分层标记和异构网络。借助上述思想，我们的方法在相应的问题上都能达到SOTA性能。这些项目的成功促使我们将工作扩大到其他更具挑战性但也很重要的问题，例如保健问题，因为这些问题可以直接造福于人民的健康和福祉。
英文摘要：
Using machine learning, especially deep learning, to facilitate biological research is a fascinating research direction. However, in addition to the standard classification or regression problems, in bioinformatics, we often need to predict more complex structured targets, such as 2D images and 3D molecular structures. The above complex prediction tasks are referred to as structured prediction. Structured prediction is more complicated than the traditional classification but has much broader applications, considering that most of the original bioinformatics problems have complex output objects. Due to the properties of those structured prediction problems, such as having problem-specific constraints and dependency within the labeling space, the straightforward application of existing deep learning models can lead to unsatisfactory results. Here, we argue that the following ideas can help resolve structured prediction problems in bioinformatics. Firstly, we can combine deep learning with other classic algorithms, such as probabilistic graphical models, which model the problem structure explicitly. Secondly, we can design the problem-specific deep learning architectures or methods by considering the structured labeling space and problem constraints, either explicitly or implicitly. We demonstrate our ideas with six projects from four bioinformatics subfields, including sequencing analysis, structure prediction, function annotation, and network analysis. The structured outputs cover 1D signals, 2D images, 3D structures, hierarchical labeling, and heterogeneous networks. With the help of the above ideas, all of our methods can achieve SOTA performance on the corresponding problems. The success of these projects motivates us to extend our work towards other more challenging but important problems, such as health-care problems, which can directly benefit people's health and wellness.
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