Bidirectional Dynamics for Protein Secondary Structure Prediction

For certain categories of sequences, information from both the past and the future can be used for analysis and predictions at time t. This is the case for biological sequences where the nature and function of a region in a sequence may strongly depend on events located both upstream and downstream. We develop a new family of adaptive graphical model architectures for learning non-causal sequence translations. These architectures employ two chains of hidden variables that propagate information from the past and from the future, respectively. This general idea can be instantiated either as a stochastic model (generalizing input output hidden Markov models), or as a neural network (generalizing recurrent neural networks). We illustrate the methodology by applying bidirectional models to the problem of protein secondary structure prediction.