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The Application of Ant Colony Optimization on Finding the Optimal Gene Order of Alzheimer’s Disease

Author: JiangGang
Tutor: PangChaoYang
School: Sichuan Normal University
Course: Computational Mathematics
Keywords: Ant Colony Algorithm Optimal sequence Gene Alzheimer's disease DNA microarrays
CLC: TP301.6
Type: Master's thesis
Year: 2011
Downloads: 21
Quote: 0
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Abstract


Alzheimer's disease is the most common form of dementia. The characteristics of this disease will lead to memory, orientation, judgment and reasoning ability of a patient's cognitive decline and eventually death. In 2006, the world has 266 million patients with Alzheimer expected every 85 individuals will be 2050, the world will have a Alzheimer patients. So far, however, have yet to find a particularly effective measures to treat Alzheimer's disease. Thorough treatment of Alzheimer's disease, we must be able to understand the pathogenesis of Alzheimer's disease, Alzheimer's disease will have to find disease genes. So to find disease genes in Alzheimer's disease is currently a major study. Currently looking for genes that cause Alzheimer's disease taken, is looking for a candidate disease genes for Alzheimer's disease through the analysis of DNA microarray data. Analysis of DNA microarray data, which method is to express similar genes together (because they are likely to be similar in function). Traditional clustering methods focus only on the clustering results, ignored the issues of class in the arrangement of the data, however, functionally similar gene together must consider the arrangement of genes. Therefore, this article will first solve the Alzheimer's disease global optimal arrangement into TSP problem, then the introduction of the currently popular smart algorithms - ACO (Ant Colony Optimization) and its improved algorithm to solve this problem. The main contents are as follows:? (1) Summary of Alzheimer's disease, gene chip technology, looking for Alzheimer's disease gene research status, as well as the main method of the application of global optimization idea for solving the optimal DNA microarrays sequence . ? (2) basic ant colony algorithm is first applied on the yeast gene data processing, and analysis of the pros and cons of the three metric in solving the optimal gene sequence; then introduce information entropy and ant colony algorithm end criterion, intelligent judge the number of iterations of the ant colony algorithm to reduce the running time, and the experimental results, we can conclude that the more obvious advantages of improved ant colony algorithm in large-scale data processing; Finally we ant colony is divided into two reduce ant colony scale class, and in the iterative process, and then also as information entropy convergence criterion, greatly accelerate the running speed while almost consistent with the basic ant colony algorithm solution. ?

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