
Predict and Constrain: Modeling Cardinality in Deep Structured Prediction
Many machine learning problems require the prediction of multidimension...
read it

Structured Prediction Energy Networks
We introduce structured prediction energy networks (SPENs), a flexible f...
read it

Learning Constraints for Structured Prediction Using Rectifier Networks
Various natural language processing tasks are structured prediction prob...
read it

Conditional Restricted Boltzmann Machines for Structured Output Prediction
Conditional Restricted Boltzmann Machines (CRBMs) are rich probabilistic...
read it

Deep transformation models: Tackling complex regression problems with neural network based transformation models
We present a deep transformation model for probabilistic regression. Dee...
read it

Learning to Predict Combinatorial Structures
The major challenge in designing a discriminative learning algorithm for...
read it

Deep Structured Prediction with Nonlinear Output Transformations
Deep structured models are widely used for tasks like semantic segmentat...
read it
Towards Structured Prediction in Bioinformatics with Deep Learning
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 problemspecific 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 problemspecific 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 healthcare problems, which can directly benefit people's health and wellness.
READ FULL TEXT
Comments
There are no comments yet.