Sequence Translating Model Using Deep Neural Block Cascade Network: Taking Protein Secondary Structure Prediction as an Example

The sequence data is one of the most common types the objects are described with and is also an important research subject that has caused a lot of attention to researchers. Mining hidden information from the sequence data is an important issue. Recently, the rapid developing deep learning technology achieves great success in biomedical information mining. In this paper, we propose a Deep Neural Block Cascade Networks(DeepNBCN) which is a general framework for sequence translating. Then we take protein secondary structure prediction as an important test target. DeepNBCN has a full-free adjustment mechanism of deep neural networks, in which every layer is structured as a block composed of one or more Feature Extractor named as module, and delivers features to the upper layer. DeepNBCN can model not only internal relations between amino acid sequence and Protein Secondary Structure(PSS), but also complex sequence relationship and dependency. We evaluate our model with two protein datasets. With a well designed architecture for PSS, the experimental results show that our model can achieve a better performance compared with the state of the art algorithms.