Bidirectional Dynamics for Protein Secondary Structure Prediction
In this chapter, we have proposed two novel architectures for dealing with sequence learning problems in which data is not obtained from physical measurements over time. The new architectures remove the causality assumption that characterize current connectionist approaches to learning sequential translations. Using bidirectional recurrent neural networks (BRNNs) on the protein secondary structure prediction task appears to be very promising. Our performance is very close to the best existing systems although our usage of profiles is not as sophisticated. One improvement of our prediction system could be obtained by using profiles from the TrEMBL database.