# Sample Reduction Strategies for Protein Secondary Structure Prediction
###### Abstract  
Predicting the secondary structure from protein sequence plays a crucial role in estimating the 3D structure, which has applications in drug design and in understanding the function of proteins. As new genes and proteins are discovered, the large size of the protein databases and datasets that can be used for training prediction models grows considerably. A two-stage hybrid classifier, which employs dynamic Bayesian networks and a support vector machine (SVM) has been shown to provide state-of-the-art prediction accuracy for protein secondary structure prediction. However, SVM is not efficient for large datasets due to the quadratic optimization involved in model training. In this paper, two techniques are implemented on CB513 benchmark for reducing the number of samples in the train set of the SVM. The first method randomly selects a fraction of data samples from the train set using a stratified selection strategy. This approach can remove approximately 50% of the data samples from the train set and reduce the model training time by 73.38% on average without decreasing the prediction accuracy significantly. The second method clusters the data samples by a hierarchical clustering algorithm and replaces the train set samples with nearest neighbors of the cluster centers in order to improve the training time. To cluster the feature vectors, the hierarchical clustering method is implemented, for which the number of clusters and the number of nearest neighbors are optimized as hyper-parameters by computing the prediction accuracy on validation sets. It is found that clustering can reduce the size of the train set by 26% without reducing the prediction accuracy. Among the clustering techniques [PERSON]'s method provided the best accuracy on test data. protein secondary structure prediction; support vector machine; bayesian network; stratified sampling; hierarchical clustering +
Footnote †: journal: Applied  
There are millions of amino acid sequences in protein databases and it is essential to annotate them according to their structural and functional roles [3]. For instance, predicting the one-dimensional properties of proteins such as secondary structure and solvent accessibility plays a crucial role in predicting the 3D structure and understanding the function of proteins [4; 5]. Several classification methods have been proposed in the literature for this purpose such as neural networks [6; 7], support vector machines [8], dynamic Bayesian networks [9] and hybrid methods that combine different classifiers [9; 10]. To date, most of the research efforts in this field have concentrated on developing advanced prediction methods. In the mean time, as new genes and proteins are discovered, the size of the protein databases and datasets that can be used for training prediction models grows considerably. Therefore efficient algorithms and/or data reduction strategies should be developed that can circumvent the computational cost caused by big data conditions while incorporating the useful information into prediction models. Though there are methods developed for reducing the number of features (i.e., dimensions) of the classifiers by employing feature selection or dimension reduction techniques [11], to the best of our knowledge, there is no work in the literature for reducing the number of train set samples using techniques such as sampling and clustering for predicting one-dimensional structural properties of proteins. Recently, a new database called UniClust has been introduced that is derived by clustering millions of proteins [12]. This database is introduced as the sequence database of the HHblits method [13], which aligns a query protein against the amino acid sequences in the database. There are also other databases introduced earlier such as SCOP [14] and PFAM [15] that organize proteins hierarchically into multiple levels (e.g., family, superfamily, fold, class, domain or clan). Among those SCOP assigns proteins to families based on multiple criteria and using clustering.