Implementation of a Particle Swarm Optimization-based Classification Algorithm for Analyzing DNA Chip Data

DNA chips are used for experiments on genes and provide useful information that could be further analyzed. Using the data extracted from the DNA chips to find useful patterns or information has become a very important issue. In this paper, we explain the application developed for classifying DNA chip data using a classification method based on the Particle Swarm Optimization (PSO) algorithm. Considering that DNA chip data is extremely large and has a fuzzy characteristic, an algorithm that imitates the ecosystem such as the PSO algorithm is suitable to be used for analyzing such data. The application enables researchers to customize the PSO algorithm parameters and see detail results of the classification rules.

A Clustering Tool Using Particle Swarm Optimization for DNA Chip Data

DNA chips are becoming increasingly popular as a convenient way to perform vast amounts of experiments related to genes on a single chip. And the importance of analyzing the data that is provided by such DNA chips is becoming significant. A very important analysis on DNA chip data would be clustering genes to identify gene groups which have similar properties such as cancer. Clustering data for DNA chips usually deal with a large search space and has a very fuzzy characteristic. The Particle Swarm Optimization algorithm which was recently proposed is a very good candidate to solve such problems. In this paper, we propose a clustering mechanism that is based on the Particle Swarm Optimization algorithm. Our experiments show that the PSO-based clustering algorithm developed is efficient in terms of execution time for clustering DNA chip data, and thus be used to extract valuable information such as cancer related genes from DNA chip data with high cluster accuracy and in a timely manner.

Characteristics of major depressive disorder according to family history of depression : A CRESCEND-K (Clinical Research Center for Depression in Korea) study / 한국역학회지

PURPOSE: People with a family history of mood disorder are more likely to have depression. This study compared the characteristics of non-psychotic major depression disorder according to family history of depression. METHOD: Subjects were total of 817 persons recruited for the CRESCEND-K multicenter trial. Characteristics of depression and suicide history of patients with and without a family history of depression were assessed. Family history was determined through self-report. RESULTS: Of 817 participants, 12.4% had a positive family history of depression. Those with family history of depression reported an earlier age at onset of MDD, and more psychiatric comorbidity. Severity of depression and anxiety were not different according to family history of depression. There were no difference in attempted suicide history, number of attempted suicide and age at onset of 1st attempted suicide according to such a family history. CONCLUSION: Patients with family history of depression reported earlier onset of MDD and more history of psychiatric comorbidity.