Essentially, Mdm2 it is a viewpoint of ensemble learning [22�C25]. Using this idea to the topology prediction of transmembrane protein, various prediction algorithms have been treat as basic predictors; the task is the combination of multiple predictors to obtain a combination predictor which has a better performance than basic predictors. Within this process, there are two critical problems, that is, the representation of each predictor’s prediction results and the combination method of combining multiple predictors. In regard to the representation of predictor’s prediction results, as Xu et al. [23] pointed three types of output information can be utilized for different prediction algorithms, namely, the information in the abstract level, rank level, and measurement level, respectively.

As to the combination method, traditional methodologies are usually on the basis of the framework of probability theory. To some degree, it is very effective, especially for the randomness. However, in the real world there are various uncertainties, not only the randomness but also the fuzziness and incompleteness, and so forth [26, 27].As a theory of evidential reasoning under the uncertain environment, the Dempster-Shafer theory of evidence [28, 29] has an advantage of directly expressing various uncertainties and has been widely used in many fields [30�C37]. It provides a general and effective framework for the representation and combination of multiple individual algorithms. In this paper, a new topology prediction method of transmembrane protein based on evidential reasoning approach, called TOPPER, has been proposed.

In the proposed TOPPER method, the prediction results of basic predictor are represented by basic probability assignment (BPA) which has been constructed in terms of the confusion matrix of the predictor. Then, various basic predictors are combined by using the Dempster’s rule of combination. Finally, the topology of a transmembrane protein sequence Carfilzomib are determined according to the combination prediction results. In this paper, an experiment demonstrates the effectiveness of the propose prediction method.The rest of this paper is organized as follows. Section 2 introduces some basic concepts about the Dempster-Shafer theory of evidence. In Section 3 the proposed method is presented. Section 4 gives experimental verification to demonstrate the effectiveness of the proposed method. Conclusions are given in Section 5.2. PreliminariesIn this section, a few concepts commonly in the Dempster-Shafer theory of evidence will be introduced.The Dempster-Shafer theory of evidence [28, 29], also called the Dempster-Shafer theory or evidence theory, is used to deal with uncertain information.