Study Of The Biclustering Algorithm Based On PSO

Bioinformatics and computational biology are interdisciplinary fields that combine the knowledge acquired in biology,computer science,mathematics,and chemistry.And they are quickly becoming disciplines in themselves with academic programs dedicated to them.With the rapid development of science and technology,gene sequencing has become a reality,and people also gradually have begun to research the functions and internal mechanism of gene.In this era of data explosion,vast amounts of genetic information data are produced every day.The focus of life science research has been shifted from how to obtain biological data to how to analyze these data effectively.At present,the main direction of gene expression data analysis and processing is clustering,which is based on the characteristics of the samples or variables to find the similar objects and put them together.Most clustering methods are based on all the data attributes,and we call them the traditional clustering.Traditional clustering can only find the global information,but cannot find local information.However,many important patterns are hidden in the local area of gene data matrix.To solve this problem and search the local information in the data matrix,the concept of biclustering is put forward and has been increasingly widely used.But these existing biclustering algorithms still have some shortcomings and deficiencies,so it is necessary to study the biclustering problems.The purpose of this paper is to use particle swarm optimization algorithm to solve the biclustering problems,and prove the advantages of our algorithms through a series of experiments.The main work of this paper is as follows:(1)Biclustering algorithm is a local search algorithm.It is obviously unreasonable that we cluster the complex genetic data matrix directly.Large amount of calculation will be needed and clustering effect is not ideal.Based on particle swarm optimization,we use the TWCV as fitness function to do the global search.So,some similarities are found in the gene sub-matrix,and then add or delete rows(columns)to its ranks.Thus,the structure of biclustering can be more regular,which avoids the imbalanced classification of gene expression data.(2)Biclustering algorithm is a multi-objective optimization algorithm,FLOC algorithm as one of the classical biclustering algorithm,but it cannot be good at the same time to optimize multiple objectives.Combined with the PSO algorithm,and modified the objective function of the FLOC,we propose the PSO-FLOC biclustering algorithm.The experiment results show that the PSO-FLOC algorithm performs better on multi-objective optimization problem.(3)In PSO algorithm,the particles can only search along a particular trajectory,and cannot converge to the global optimum with probability 1,sometimes even cannot converge to local optimum.To improve the global search capability of the algorithm,we use particle swarm optimization with quantum behavior,and propose the QPSO-FLOC clustering algorithm.Through experiments,QPSO-FLOC algorithm can achieve better clustering effect than PSO algorithm.