Preditcing protein subcellular location by AdaBoost.M1 algorithm

In bioinformatics fields, Predicting protein subcellular location is an important task, because protein has to be located in its proper position in a cell to perform its biological functions. Therefore, predicting protein location is an important and challenging task in current molecular and cellular biology. In this paper, a computational method based AdaBoost.M1 algorithm and pseudo amino acids composition (PseAAC) to identify protein subcellular location. AdaBoost.M1, an improved algorithm directly extends the original AdaBoost algorithm to the multi-class case without reducing it to multiple two-class problems, is applied to predict the protein subcellular location. In some previous studies conventional amino acid composition is applied to represent a protein. In order to take into account sequence order effects, in this study we use PseAAC that was proposed by Chou instead of convention amino acids composition to represent a protein. To demonstrate AdaBoost.M1 is a robust and efficient model in predicting location, the same protein dataset that was used cedano et al. in 1997 is adopted by us in this paper. From the result, we can draw a conclusion that the accuracy of this method is outperformed than other methods used by previous researchers and can make the prediction into practice.