Abstract
In the field of bioinformatics, understanding protein secondary structure is very important for exploring diseases and finding new treatments. Considering that the physical experiment-based protein secondary structure prediction methods are time-consuming and expensive, some pattern recognition and machine learning methods are proposed. However, most of the methods achieve quite similar performance, which seems to reach a model capacity bottleneck. As both model design and learning process can affect the model learning capacity, we pay attention to the latter part. To this end, a framework called Multistage Combination Classifier Augmented Model (MCCM) is proposed to solve the protein secondary structure prediction task. Specifically, first, a feature extraction module is introduced to extract features with different levels of learning difficulties. Second, multistage combination classifiers are proposed to learn decision boundaries for easy and hard samples, respectively, with the latter penalizing the loss value of the hard samples and finally improving the prediction performance of hard samples. Third, based on the Dirichlet distribution and information entropy measurement, a sample difficulty discrimination module is designed to assign samples with different learning difficulty levels to the aforementioned classifiers. The experimental results on the publicly available benchmark CB513 dataset show that our method outperforms most state-of-the-art models.