Protein's subcellular location, which indicates where a protein resides in a cell, is an important characteristic of protein. Correctly assigning proteins to their subcellular locations would be of great help to the prediction of proteins' function, genome annotation, and drug design. Yet, in spite of great technical advance in the past decades, it is still time-consuming and laborious to experimentally determine protein subcellular locations on a high throughput scale. Hence, four integrated-algorithm methods were developed to fulfill such high throughput prediction in this article. Two data sets taken from the literature (Chou and Elrod, Protein Eng 12:107-118, 1999) were used as training set and test set, which consisted of 2,391 and 2,598 proteins, respectively. Amino acid composition was applied to represent the protein sequences. The jackknife cross-validation was used to test the training set. The final best integrated-algorithm predictor was constructed by integrating 10 algorithms in Weka (a software tool for tackling data mining tasks, http://www.cs.waikato.ac.nz/ml/weka/ ) based on an mRMR (Minimum Redundancy Maximum Relevance, http://research.janelia.org/peng/proj/mRMR/ ) method. It can achieve correct rate of 77.83 and 80.56% for the training set and test set, respectively, which is better than all of the 60 algorithms collected in Weka. This predicting software is available upon request.