  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.
