Gene Microarray Data Analysis Based On Clustering Algorithms

Bioinformatics is an interdisciplinary approach drawing from specific disciplines such as biology, computer science and mathematics. It raised with human genome project at the end of 80's. During the process of explaining the essence of life, it is already known that not only the genetic information but also the expression regulation of genetic information is very important for organisms. So the analysis of gene expression data becomes the key to discover the mystery of life. The Gene Chip or Microarray is a latest breakthrough of the experimental techniques for molecular biology. Microarray can simultaneously analyze the expression data of thousands of genes and thereby generate a large quantity of available information. How to find the regulation relationship among genes from these data and moreover, the molecular mechanisms under biological phenomena is the difficult but a hot area in bioinformatics.Clustering analysis commonly called Clustering is a kind of multi-statistic analysis and is a part of important data mining. It is also an important method of data partition and grouping. The goal of clustering is to partition samples set into such clusters that intra-clustering samples are similar and inter-cluster samples are dissimilar with out any prior knowledge, which bases on samples'comparability. So far clustering analysis has become an important analysis procedure for gene expression data.The main working have completed in the paper as follows:(1)To enable the reader to understand the microarray data in the main, the author introduces the knowledge about bioinformatics, gene chip, microarray technique.(2)The basic principle of the clustering analysis is discussed, which includes the clustering theory, the clustering methods and their applications.(3)The basic principle of the fuzzy clustering algorithm is discussed in detail as well as its advantages and drawbacks. Based on above theories, the new clustering analysis method is put forward in this paper. Through the testing, new method is proved to be effective to solve clustering problems.(4)The basic principle of the ant colony algorithm is discussed in detail as well as its advantages and drawbacks. Based on above theories, the new clustering analysis method, which based on improved ant colony algorithm, is put forward in this paper. Then the author realizes the new method and design an emulator. (5)With the emulator, the new method is compared with other clustering analysis algorithms based on two typical microarray datasets. Through trail and testing, it turns out to be effective to solve clustering analysis problems of the microarray data.At last, we conclude and analyze current research work, discuss our intending research work in this area and prospect the ant colony clustering algorithm.