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Two Improved Algorithms for Multiple Sequence Alignment in a Remote Diagnose System for Colonic Cancer in Pervasive Environment

This paper presents our recent research work on a remote diagnoses system for colonic cancer in pervasive environment. In the system, the high quality of multiple protein sequence alignment for patients plays a crucial role in doctors' correct decision-making. This paper laid stress on improving the performance of multiple sequence alignment. Two improved evolutionary algorithms are proposed here. One is based on a genetic algorithm, where segment profiles are introduced to speed up convergence. The other one is an application of an improved particle swarm optimization algorithm, where the principles of information diffusion and clone selection are incorporated to prevent premature convergence. The two new algorithms are compared with the ClustalX and T-Coffee programs on several data cases from the BAHBASE benchmark alignment database. The experimental results show that they can yield better performance on data sets and suit multiple alignment of protein sequences with different length and similarity