Abstract: An active learning method using SVM was introduced in this paper to solve the problem of protein-protein interaction prediction task．Biological processes in cells are carried out through protein-protein interactions.Since determining whether a pair of genes interacts by wet-lab experiments is resource-intensive,we proposed a support vector machine active learning algorithm for interaction prediction.Active machine learning can guide the selection of pairs of genes for future experimental characterization in order to accelerate accurate prediction of the human gene interactome．As a method of constructing an effective training set,the goal of active learning algorithm is to find informative sample which can enhance the classification results of the model during the iteration,thereby reducing the size of the training set and improving the efficiency of the model within limited time and resources.The experiment shows that compared with the general SVM,active learning with SVM can reduce the number of examples effectively on the premise of keeping correctness of the classifier.

Key words: Support vector machine,Active learning,Protein-protein interaction