3207967
ArrayXPath: mapping and visualizing microarray gene-expression data with integrated biological pathway resources using Scalable Vector Graphics
Biological pathways can provide key information on the organization of biological systems. ArrayXPath (http://www.snubi.org/software/ArrayXPath/) is a web-based service for mapping and visualizing microarray gene-expression data for integrated biological pathway resources using Scalable Vector Graphics (SVG). By integrating major bio-databases and searching pathway resources, ArrayXPath automatically maps different types of identifiers from microarray probes and pathway elements. When one inputs gene-expression clusters, ArrayXPath produces a list of the best matching pathways for each cluster. We applied Fisher's exact test and the false discovery rate (FDR) to evaluate the statistical significance of the association between a cluster and a pathway while correcting the multiple-comparison problem. ArrayXPath produces Javascript-enabled SVGs for web-enabled interactive visualization of pathways integrated with gene-expression profiles.
3457933
On the Society of Genome: Social Affiliation Network Analysis of Microarray Data
To investigate the structure of the genomic interaction network built from yeast gene-expression compendium dataset of hundreds of systematic perturbations, social affiliation network analysis methodologies were applied through quantifying various density, closeness and centrality measures and exploring core-periphery structures. Genes affected by a larger number of perturbations were found to be involved in responses to various environmental challenges. Deletion of essential genes was suggested to cause larger number of genes to be significantly up or down regulated. We explored the network structure made up of several sub-networks using core-periphery models to find ancient pathways. Glycolysis and TCA cycle have relatively core positions in the energy-related processes of yeast.
12688706
Unsupervised Learning from Complex Data: The Matrix Incision Tree Algorithm
Analysis of large-scale gene expression data requires novel methods for knowledge discovery and predictive model building as well as clustering. Organizing data into meaningful structures is one of the most fundamental modes of learning. DNA microarray data set can be viewed as a set of mutually associated genes in a high-dimensional space. This paper describes a novel method to organize a complex high-dimensional space into successive lower-dimensional spaces based on the geometric properties of the data structure in the absence of a priori knowledge. The matrix incision tree algorithm reveals the hierarchical structural organization of observed data by determining the successive hyperplanes that 'optimally' separate the data hyperspace. The algorithm was tested against published data sets yielding promising results.
5105796
Two-stage normalization using background intensities in cDNA microarray data
BackgroundIn the microarray experiment, many undesirable systematic variations are commonly observed. Normalization is the process of removing such variation that affects the measured gene expression levels. Normalization plays an important role in the earlier stage of microarray data analysis. The subsequent analysis results are highly dependent on normalization. One major source of variation is the background intensities. Recently, some methods have been employed for correcting the background intensities. However, all these methods focus on defining signal intensities appropriately from foreground and background intensities in the image analysis. Although a number of normalization methods have been proposed, no systematic methods have been proposed using the background intensities in the normalization process.ResultsIn this paper, we propose a two-stage method adjusting for the effect of background intensities in the normalization process. The first stage fits a regression model to adjust for the effect of background intensities and the second stage applies the usual normalization method such as a nonlinear LOWESS method to the background-adjusted intensities. In order to carry out the two-stage normalization method, we consider nine different background measures and investigate their performances in normalization. The performance of two-stage normalization is compared to those of global median normalization as well as intensity dependent nonlinear LOWESS normalization. We use the variability among the replicated slides to compare performance of normalization methods.ConclusionsFor the selected background measures, the proposed two-stage normalization method performs better than global or intensity dependent nonlinear LOWESS normalization method. Especially, when there is a strong relationship between the background intensity and the signal intensity, the proposed method performs much better. Regardless of background correction methods used in the image analysis, the proposed two-stage normalization method can be applicable as long as both signal intensity and background intensity are available.
731257
A Novel Framework for Discovering Robust Cluster Results
We propose a novel method, called heterogeneous clustering ensemble (HCE), to generate robust clustering results that combine multiple partitions (clusters) derived from various clustering algorithms. The proposed method combines partitions of various clustering algorithms by means of newly-proposed the selection and the crossover operation of the genetic algorithm (GA) during the evolutionary process.
2978705
Reconstructing the Temporal Ordering of Biological Samples Using Microarray Data
MOTIVATION
Accurate time series for biological processes are difficult to estimate due to problems of synchronization, temporal sampling and rate heterogeneity. Methods are needed that can utilize multi-dimensional data, such as those resulting from DNA microarray experiments, in order to reconstruct time series from unordered or poorly ordered sets of observations.


RESULTS
We present a set of algorithms for estimating temporal orderings from unordered sets of sample elements. The techniques we describe are based on modifications of a minimum-spanning tree calculated from a weighted, undirected graph. We demonstrate the efficacy of our approach by applying these techniques to an artificial data set as well as several gene expression data sets derived from DNA microarray experiments. In addition to estimating orderings, the techniques we describe also provide useful heuristics for assessing relevant properties of sample datasets such as noise and sampling intensity, and we show how a data structure called a PQ-tree can be used to represent uncertainty in a reconstructed ordering.


AVAILABILITY
Academic implementations of the ordering algorithms are available as source code (in the programming language Python) on our web site, along with documentation on their use. The artificial 'jelly roll' data set upon which the algorithm was tested is also available from this web site. The publicly available gene expression data may be found at http://genome-www.stanford.edu/cellcycle/ and http://caulobacter.stanford.edu/CellCycle/.
1639978
ViChaR: A Dynamic Virtual Channel Regulator for Network-on-Chip Routers
The advent of deep sub-micron technology has recently highlighted the criticality of the on-chip interconnects. As diminishing feature sizes have led to increases in global wiring delays, network-on-chip (NoC) architectures are viewed as a possible solution to the wiring challenge and have recently crystallized into a significant research thrust. Both NoC performance and energy budget depend heavily on the routers' buffer resources. This paper introduces a novel unified buffer structure, called the dynamic virtual channel regulator (ViChaR), which dynamically allocates virtual channels (VC) and buffer resources according to network traffic conditions. ViChaR maximizes throughput by dispensing a variable number of VCs on demand. Simulation results using a cycle-accurate simulator show a performance increase of 25% on average over an equal-size generic router buffer, or similar performance using a 50% smaller buffer. ViChaR's ability to provide similar performance with half the buffer size of a generic router is of paramount importance, since this can yield total area and power savings of 30% and 34%, respectively, based on synthesized designs in 90 nm technology
16087620
A Corpus-Based Learning Method of Compound Noun Indexing Rules for Korean
In Korean information retrieval, compound nouns play an important role in improving precision in search experiments. There are two major approaches to compound noun indexing in Korean: statistical and linguistic. Each method, however, has its own shortcomings, such as limitations when indexing diverse types of compound nouns, over-generation of compound nouns, and data sparseness in training. In this paper, we propose a corpus-based learning method, which can index diverse types of compound nouns using rules automatically extracted from a large corpus. The automatic learning method is more portable and requires less human effort, although it exhibits a performance level similar to the manual-linguistic approach. We also present a new filtering method to solve the problems of compound noun over-generation and data sparseness.
12043862
A method and tool support for variant requirements analysis: goal and scenario based approach
Identifying and representing variant requirements among products in a product family are crucial activities for a successful software reuse. The variant requirements should be not only identified based on the business goal, which drives marketing plan, product plan, and differences among products, but also represented as familiar notations in order to support developing a particular product in the product family. Thus, our proposal is to identify the variant requirements through goals and scenarios, and represent them as variable use cases for a product family. Especially, for identification of the variant requirements, we propose four abstraction levels of requirements in the product family, and goal and scenario modeling. For representation of them, variable use case model is suggested, and also the use case transfer rules are proposed so as to bridge the gap between the identification and representation activity. The paper illustrates the application of the approach within a supporting tool, namely DREAM (Domain REquirements Analysis Method) using the ATM example.
205099619
Simulated annealing and genetic algorithms for scheduling products with multi-level product structure
We consider a short-term production scheduling problem in a manufacturing system producing products with multi-level product structure, and their components and subassemblies. The problem is to schedule production of components, subassemblies, and final products with the objective of minimizing the weighted sum of tardiness and earliness of the items. We consider due dates of final products, precedence relationships among items, and processing capacity of the manufacturing system. The scheduling problem is solved with simulated annealing and genetic algorithms, which are search heuristics often used for global optimization in a complex search space. To compare the performance of these algorithms with the finite loading method, which is often used in practice, computational experiments are carried out using randomly generated test problems and results are reported.