10882838
A multivariate prediction model for microarray cross-hybridization
BackgroundExpression microarray analysis is one of the most popular molecular diagnostic techniques in the post-genomic era. However, this technique faces the fundamental problem of potential cross-hybridization. This is a pervasive problem for both oligonucleotide and cDNA microarrays; it is considered particularly problematic for the latter. No comprehensive multivariate predictive modeling has been performed to understand how multiple variables contribute to (cross-) hybridization.ResultsWe propose a systematic search strategy using multiple multivariate models [multiple linear regressions, regression trees, and artificial neural network analyses (ANNs)] to select an effective set of predictors for hybridization. We validate this approach on a set of DNA microarrays with cytochrome p450 family genes. The performance of our multiple multivariate models is compared with that of a recently proposed third-order polynomial regression method that uses percent identity as the sole predictor. All multivariate models agree that the 'most contiguous base pairs between probe and target sequences,' rather than percent identity, is the best univariate predictor. The predictive power is improved by inclusion of additional nonlinear effects, in particular target GC content, when regression trees or ANNs are used.ConclusionA systematic multivariate approach is provided to assess the importance of multiple sequence features for hybridization and of relationships among these features. This approach can easily be applied to larger datasets. This will allow future developments of generalized hybridization models that will be able to correct for false-positive cross-hybridization signals in expression experiments.
32363
Optimal cDNA microarray design using expressed sequence tags for organisms with limited genomic information
BackgroundExpression microarrays are increasingly used to characterize environmental responses and host-parasite interactions for many different organisms. Probe selection for cDNA microarrays using expressed sequence tags (ESTs) is challenging due to high sequence redundancy and potential cross-hybridization between paralogous genes. In organisms with limited genomic information, like marine organisms, this challenge is even greater due to annotation uncertainty. No general tool is available for cDNA microarray probe selection for these organisms. Therefore, the goal of the design procedure described here is to select a subset of ESTs that will minimize sequence redundancy and characterize potential cross-hybridization while providing functionally representative probes.ResultsSequence similarity between ESTs, quantified by the E-value of pair-wise alignment, was used as a surrogate for expected hybridization between corresponding sequences. Using this value as a measure of dissimilarity, sequence redundancy reduction was performed by hierarchical cluster analyses. The choice of how many microarray probes to retain was made based on an index developed for this research: a sequence diversity index (SDI) within a sequence diversity plot (SDP). This index tracked the decreasing within-cluster sequence diversity as the number of clusters increased. For a given stage in the agglomeration procedure, the EST having the highest similarity to all the other sequences within each cluster, the centroid EST, was selected as a microarray probe. A small dataset of ESTs from Atlantic white shrimp (Litopenaeus setiferus) was used to test this algorithm so that the detailed results could be examined. The functional representative level of the selected probes was quantified using Gene Ontology (GO) annotations.ConclusionsFor organisms with limited genomic information, combining hierarchical clustering methods to analyze ESTs can yield an optimal cDNA microarray design. If biomarker discovery is the goal of the microarray experiments, the average linkage method is more effective, while single linkage is more suitable if identification of physiological mechanisms is more of interest. This general design procedure is not limited to designing single-species cDNA microarrays for marine organisms, and it can equally be applied to multiple-species microarrays of any organisms with limited genomic information.
17642380
Evolutionary Modeling of Ordinary Differential Equations for Dynamic Systems
This paper presents a new idea of modeling one-dimensional dynamic systems by higher-order ordinary differential equation (HODE) models in stead of by the ARMA models used in the traditional time series analysis. Accordingly, based on the idea of hybrid evolutionary modeling, we modify the HEMA algorithm in (Cao et al., 1998a; Cao et al., 1998b) to approach the modeling problem of HODEs for dynamic systems. Two applications of time series are used to demonstrate its effectiveness and advantages.
2439055
Active brokers and their runtime deployment in the ECho/JECho distributed event systems
This paper introduces active brokers and third-party derivation, which is the basic programming construct for run-time remote broker deployment, in the ECho/JECho distributed event systems. We describe its implementation in the JECho system and give examples of using it in ECho/JECho distributed event systems. In particular, we describe the use of third-party derivation in supporting the scalability of content-based event delivery. Specifically, third-party derivation is used both to dynamically construct content-based event distribution trees and to offload potentially expensive client-specific event routing/processing by the run-time creation of remote brokers. Our preliminary benchmark results demonstrate the significant benefits of using third-party derivation.
17475296
Statistical geometrical features for texture classification
Abstract This paper proposes a novel set of 16 features based on the statistics of geometrical attributes of connected regions in a sequence of binary images obtained from a texture image. Systematic comparison using all the Brodatz textures shows that the new set achieves a higher correct classification rate than the well-known Statistical Gray Level Dependence Matrix method, the recently proposed Statistical Feature Matrix, and Liu's features. The deterioration in performance with the increase in the number of textures in the set is less with the new SGF features than with the other methods, indicating that SGF is capable of handling a larger texture population. The new method's performance under additive noise is also shown to be the best of the four.
16186547
Specification-based regression test selection with risk analysis
Regression testing is essential to ensure software quality. The test team applies a regression test suite to ensure that new or modified features do not regress (make worse) existing features. Although existing research has addressed many problems and put forward solutions, most regression test techniques are code-based. Code-based regression test selection is good for unit testing, but it has a scalability problem. When the size of the subject under test grows, it becomes hard to manage all the information and to create corresponding traceability matrices. In this paper, we describe a specification-based method for regression test selection.The basic model we use for describing requirements based on customer features or behaviors is the activity diagram, which is a notation of the Unified Modeling Language (UML). A process for identifying the affected test cases is presented. To summarize our approach, we select two kinds of regression tests: i) Targeted Tests, which ensure that important current customer features are still supported adequately in the new release and ii) Safety Tests, which are risk-directed, and ensure that potential problem areas are properly handled. Our test selection technique will be based on a practical risk analysis model.
7576060
Specification-Enhanced Policies for Automated Management of Changes in IT Systems
Enterprise and grid computing systems are complex and subject to a broad range of changes such as configuration updates, failures, and performance degradations. These changes affect infrastructure elements such as computation and storage nodes, applications, and system management elements such as monitoring infrastructures. Today's best practices in use by system administrators to manage these changes are manual and ad-hoc. In large complex installations, this would lead to high operational costs, broken closed loop automation, and reduced agility. Providing tools and mechanisms to administrators that automate the reaction to these changes is highly desirable and is an active research area. 
 
Policy-based management using Event-Condition-Action (ECA) rules is a well-known approach for such automated change management where management actions are executed when specified event-conditions are observed. In complex systems, the interdependence of components generates multiple events when a single change happens causing multiple rules to be triggered. The order of execution of rule actions determines the system behavior necessitating reasoning about execution order. ECA rules do not contain explicit action specifications needed for reasoning and are therefore unsuited for specifying management rules. 
 
In this paper, we propose a specification-enhanced ECA model called Event-Condition-Precondition-Action-Postcondition (ECPAP) for designing adaptation rules. ECPAP rules contain action specifications in first order predicate logic enabling us to develop reasoning algorithms to determine enforcement order of multiple rules. The enforcement order is represented as a Boolean Interpreted Petri Net workflow. We introduce a new notion called enforcement semantics that provides guarantees about rule ordering. We have built an adaptation framework using ECPAP model and have demonstrated it for automated change management of Ganglia and HP OpenView monitoring systems. The evaluation of the framework illustrates the significance of the ECPAP model and demonstrates its applicability for managing complex IT environments.