9567759
A hybrid heuristic for DAG scheduling on heterogeneous systems
Summary form only given. This paper is motivated by the observation that different methods to compute the weights of nodes and edges when scheduling DAGs onto heterogeneous machines may lead to significant variations in the generated schedule. To minimize such variations, we present a novel heuristic for DAG scheduling, which is based upon solving a series of independent task scheduling problems. A novel heuristic for the latter problem is also included. Both heuristics compare favourably with other related heuristics.
21716413
Advance Reservation Policies for Workflows
Advance reservation of resources has been suggested as a means to provide a certain level of support that meets user expectations with respect to specific job start times in parallel systems. Those expectations may relate to a single job application or an application that consists of a collection of dependent jobs. In the context of Grid computing, applications consisting of dependent tasks become increasingly important, usually known as workflows. This paper focuses on the problem of planning advance reservations for individual tasks of workflow-type of applications when the user specifies a requirement only for the whole workflow application. Two policies to automate advance reservation planning for individual tasks efficiently are presented and evaluated.
7262387
An Experimental Investigation into the Rank Function of the Heterogeneous Earliest Finish Time Scheduling Algorithm
This paper considers the Heterogeneous Earliest Finish Time (HEFT) algorithm for scheduling the tasks of an application, represented by a directed acyclic graph, onto a bounded number of heterogeneous machines. We focus on the appropriate selection of the weight for the nodes and edges of the graph, and experiment with a number of different schemes for computing these weights. Our findings indicate that the length of the schedule produced may be affected significantly by the scheme used, and suggest that the mean value based approach used by HEFT may not be a particularly good choice.
7720203
A low-cost rescheduling policy for efficient mapping of workflows on grid systems
Workflow management is emerging as an important service in Grid computing. A simple model that can be used for the representation of certain workflows is a directed acyclic graph. Although many heuristics have been proposed to schedule such graphs on heterogeneous environments, most of them assume accurate prediction of computation and communication costs. This limits their direct applicability to a dynamically changing environment, such as the Grid. In this environment, an initial schedule may be built based on estimates, but run-time rescheduling may be needed to improve application performance. This paper presents a low-cost rescheduling policy, which considers rescheduling at a few, carefully selected points during the execution. This policy achieves performance results, which are comparable with those achieved by a policy that dynamically attempts to reschedule before the execution of every task.
1436574
Scheduling multiple DAGs onto heterogeneous systems
The problem of scheduling a single DAG onto heterogeneous systems has been studied extensively. In this paper, we focus on the problem of scheduling more than one DAG at the same time onto a set of heterogeneous resources. The aim is not only to optimize the overall makespan, but also to achieve fairness, defined on the basis of the slowdown that each DAG would experience as a result of competing for resources with other DAGs. Two policies particularly focussing to deliver fairness are presented and evaluated along with another four policies that can be used to schedule multiple DAGs.
1139392
Pathway analysis using random forests classification and regression
MOTIVATION
Although numerous methods have been developed to better capture biological information from microarray data, commonly used single gene-based methods neglect interactions among genes and leave room for other novel approaches. For example, most classification and regression methods for microarray data are based on the whole set of genes and have not made use of pathway information. Pathway-based analysis in microarray studies may lead to more informative and relevant knowledge for biological researchers.


RESULTS
In this paper, we describe a pathway-based classification and regression method using Random Forests to analyze gene expression data. The proposed methods allow researchers to rank important pathways from externally available databases, discover important genes, find pathway-based outlying cases and make full use of a continuous outcome variable in the regression setting. We also compared Random Forests with other machine learning methods using several datasets and found that Random Forests classification error rates were either the lowest or the second-lowest. By combining pathway information and novel statistical methods, this procedure represents a promising computational strategy in dissecting pathways and can provide biological insight into the study of microarray data.


AVAILABILITY
Source code written in R is available from http://bioinformatics.med.yale.edu/pathway-analysis/rf.htm.
5682481
VitaPad: visualization tools for the analysis of pathway data
MOTIVATION
Packages that support the creation of pathway diagrams are limited by their inability to be readily extended to new classes of pathway-related data.


RESULTS
VitaPad is a cross-platform application that enables users to create and modify biological pathway diagrams and incorporate microarray data with them. It improves on existing software in the following areas: (i) It can create diagrams dynamically through graph layout algorithms. (ii) It is open-source and uses an open XML format to store data, allowing for easy extension or integration with other tools. (iii) It features a cutting-edge user interface with intuitive controls, high-resolution graphics and fully customizable appearance.


AVAILABILITY
http://bioinformatics.med.yale.edu


CONTACTS
matthew.holford@yale.edu; hongyu.zhao@yale.edu.
10817560
Information assessment on predicting protein-protein interactions
BackgroundIdentifying protein-protein interactions is fundamental for understanding the molecular machinery of the cell. Proteome-wide studies of protein-protein interactions are of significant value, but the high-throughput experimental technologies suffer from high rates of both false positive and false negative predictions. In addition to high-throughput experimental data, many diverse types of genomic data can help predict protein-protein interactions, such as mRNA expression, localization, essentiality, and functional annotation. Evaluations of the information contributions from different evidences help to establish more parsimonious models with comparable or better prediction accuracy, and to obtain biological insights of the relationships between protein-protein interactions and other genomic information.ResultsOur assessment is based on the genomic features used in a Bayesian network approach to predict protein-protein interactions genome-wide in yeast. In the special case, when one does not have any missing information about any of the features, our analysis shows that there is a larger information contribution from the functional-classification than from expression correlations or essentiality. We also show that in this case alternative models, such as logistic regression and random forest, may be more effective than Bayesian networks for predicting interactions.ConclusionsIn the restricted problem posed by the complete-information subset, we identified that the MIPS and Gene Ontology (GO) functional similarity datasets as the dominating information contributors for predicting the protein-protein interactions under the framework proposed by Jansen et al. Random forests based on the MIPS and GO information alone can give highly accurate classifications. In this particular subset of complete information, adding other genomic data does little for improving predictions. We also found that the data discretizations used in the Bayesian methods decreased classification performance.
7867544
A statistical method for identifying differential gene-gene co-expression patterns
MOTIVATION
To understand cancer etiology, it is important to explore molecular changes in cellular processes from normal state to cancerous state. Because genes interact with each other during cellular processes, carcinogenesis related genes may form differential co-expression patterns with other genes in different cell states. In this study, we develop a statistical method for identifying differential gene-gene co-expression patterns in different cell states.


RESULTS
For efficient pattern recognition, we extend the traditional F-statistic and obtain an Expected Conditional F-statistic (ECF-statistic), which incorporates statistical information of location and correlation. We also propose a statistical method for data transformation. Our approach is applied to a microarray gene expression dataset for prostate cancer study. For a gene of interest, our method can select other genes that have differential gene-gene co-expression patterns with this gene in different cell states. The 10 most frequently selected genes, include hepsin, GSTP1 and AMACR, which have recently been proposed to be associated with prostate carcinogenesis. However, genes GSTP1 and AMACR cannot be identified by studying differential gene expression alone. By using tumor suppressor genes TP53, PTEN and RB1, we identify seven genes that also include hepsin, GSTP1 and AMACR. We show that genes associated with cancer may have differential gene-gene expression patterns with many other genes in different cell states. By discovering such patterns, we may be able to identify carcinogenesis related genes.
590214
The Chromaticity of Certain Complete Multipartite Graphs
Abstract.In this paper, we first establish a useful inequality on the minimum real roots of the adjoint polynomials of the complete graphs. By using it, we investigate the chromatic uniqueness of certain complete multipartite graphs. An unsolved problem (i.e., Problem 11), posed by Koh and Teo in Graph and Combin. 6(1990) 259–285, is completely solved by giving it a positive answer. Moreover, many existing results on the chromatic uniqueness of complete multipartite graphs are generalized.