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The Study on Swarm Intelligent Optimization Algorithm of RNA Secondary Structure Prediction

Author: LinJuan
Tutor: ZhongYiWen
School: Fujian Agriculture and Forestry University
Course: Bio - information science and technology
Keywords: RNA Secondary Structure Prediction Swarm intelligence optimization Particle Swarm Optimization Immune Principle Discrete leapfrog algorithm
CLC: TP301.6
Type: Master's thesis
Year: 2011
Downloads: 28
Quote: 1
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Abstract


The molecular structure determines the nature and function of the molecule, RNA of various functions to its structure is closely linked to further tap its function, it is necessary to start from a better understanding of the structure of RNA. However, the experimental method to determine the specific three-dimensional structure of the RNA to spend, difficult, and not all molecules. Therefore, the understanding of the existing structural and functional properties of the molecule, through computer simulation and calculation to \Minimum free energy algorithm is international today the most widely used prediction method based on local stem area search and evaluation function can be used as a free structure prediction for combinatorial optimization problems, swarm intelligence optimization algorithm due to its combinatorial optimization problems The excellent performance was introduced into the RNA secondary structure prediction, yielded some results. This thesis by two typical swarm intelligence optimization algorithms: classical particle swarm optimization algorithm, and in recent years the rise of the leapfrog algorithm, forecast prediction problem, the rational design algorithm framework, swarm intelligence algorithms in RNA secondary structure prediction revelation. First introduce the mathematical definition of RNA secondary structure, and the introduction of the thermodynamic model is used to forecast, to lay a theoretical and experimental basis for the prediction algorithm. Detailed analysis of the particle swarm optimization algorithm to solve the problem of RNA secondary structure prediction SetPSO algorithm, starting from the basic principle of artificial immune system, the introduction of immunological memory operator to be improved. Simulation and comparison results showed that the immune memory mechanism can effectively avoid premature stagnation, improve forecast accuracy. Re-design the framework of the implementation of discrete particle swarm optimization algorithm for RNA secondary structure prediction problem, redefine particle's position, speed and computing rules, starting from the basic principle of artificial immune system, added immune mutation operator to maintain diversity, adding vaccination vaccine operator the ability to improve the algorithm refinement. The simulation results show that the new algorithm can achieve better prediction accuracy. Introduce the principle of the leapfrog algorithm, application and improvement, discrete leapfrog algorithm designed to solve combinatorial optimization problems, to redefine the position of the individual and operation rules. Simulation results show that the discrete leapfrog algorithm for RNA secondary structure prediction problem with good accuracy. Show that these three algorithms swarm intelligence optimization algorithm is effective for solving the problem of RNA secondary structure, future research will be included in the the pseudoknot prediction and swarm intelligence parallel computing method to further improve forecast accuracy and speed.

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