Clustering methods for the analysis of DNA microarray data

Source: CiteSeer

ABSTRACT It is now possible to simultaneously measure the expression of thousands of genes during cellular differentiation and response, through the use of DNA microarrays. A major statistical task is to understand the structure in the data that arise from this technology. In this paper we review various methods of clustering, and illustrate how they can be used to arrange both the genes and cell lines from a set of DNA microarray experiments. The methods discussed are global clustering techniques including hierarchical, K-means, and block clustering, and tree-structured vector quantization. Finally, we propose a new method for identifying structure in subsets of both genes and cell lines that are potentially obscured by the global clustering approaches. 1 Introduction DNA microarrays and other high-throughput methods for analyzing complex nucleic acid samples make it now possible to measure rapidly, efficiently and accurately the levels of virtually all genes expressed in a biologi...

  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: A key issue in one-way delay measurement is the removal of relative clock offset in the situation of without external clock synchronization mechanisms for the end-to-end hosts. Most researches are based on the assumption that the clock skew retains constant and without clock adjustments and drifts during measurement. But in fact, it is found that end system clock might be subject to gradual or instantaneous clock adjustments and frequency adjustments in operation. In this paper, with the time series segmentation technology, we discuss the detection of clock dynamics in one-way delay measurement. Two algorithms are proposed to estimate the relative clock offset in post facto and on-line mode respectively, while with only unidirectional probe packets. The
    Journal of Software 01/2004;
  • Source
  • [Show abstract] [Hide abstract]
    ABSTRACT: Genetic Algorithms (GAs) are commonly used today worldwide. Various observations have been theorized about genetic algorithms regarding the mutation probability and the population size. Basically these are the search heuristics that mimic the process of natural evolution. This heuristic is routinely used to generate useful solutions for optimization and search problems. GAs belong to the larger class of evolutionary algorithms (EAs), which generate solutions to maximize problem solving by using techniques inspired by natural evolution, such as inheritance, mutation, selection, and crossover. In this paper we study of a simple heuristic in order to control the crossover probability of a GA. We will also explain how stress factors in on the crossover probability and why it is an important phenomenon in case of a GA and how it can be controlled effectively. Experimental results show that, for reaching lower probability from higher probability, we can get faster optimal solutions for any problem. These experimental values are derived by taking the values at the high probability and then slowly yet steadily decreasing them.