Tri-Cluster-Tri-Scheme-Training: Exploiting Unlabeled Data for Transmembrane Segments Prediction

Recent work using supervised learning for protein structure prediction has achieved state-of-the-art classification performance. However, such methods are based only on labeled data, while in practice the labeled data is so few and expensive to obtain and unlabeled data is far more plentiful. An effective way to enhance the performance of the learned hypothesis by using the labeled and unlabeled data together is known as semi-supervised learning. Although there are lots of semi-supervised learning methods, those approaches could not always achieve the acceptable results for bioinformatics application, especially when there is only very few labeled instances. Therefore, in this paper, we present a novel, more effective method tri-cluster-tri-scheme-training (TCTS) which firstly uses tri-cluster to label some high confidence unlabeled instances and then refines the classifiers by utilizing both of the label data and unlabeled data in the Tri-Scheme-training by different schemes. The encouraging experimental results indicate that TCTS algorithm opens a new way to solve the complex classification problem when very few labeled datasets are available.