Title: Improving Protein Gamma-Turn Prediction Using Inception Capsule Networks,
Abstract: Protein gamma-turn prediction is useful in protein function studies and
experimental design. Several methods for gamma-turn prediction have been
developed, but the results were unsatisfactory with Matthew correlation
coefficients (MCC) around 0.2-0.4. One reason for the low prediction accuracy
is the limited capacity of the methods; in particular, the traditional
machine-learning methods like SVM may not extract high-level features well to
distinguish between turn or non-turn. Hence, it is worthwhile exploring new
machine-learning methods for the prediction. A cutting-edge deep neural
network, named Capsule Network (CapsuleNet), provides a new opportunity for
gamma-turn prediction. Even when the number of input samples is relatively
small, the capsules from CapsuleNet are very effective to extract high-level
features for classification tasks. Here, we propose a deep inception capsule
network for gamma-turn prediction. Its performance on the gamma-turn benchmark
GT320 achieved an MCC of 0.45, which significantly outperformed the previous
best method with an MCC of 0.38. This is the first gamma-turn prediction method
utilizing deep neural networks. Also, to our knowledge, it is the first
published bioinformatics application utilizing capsule network, which will
provides a useful example for the community.