8241163
MS-BID: a Java package for label-free LC-MS-based comparative proteomic analysis
UNLABELLED
MS-BID (MS Biomarker Discovery Platform) is an integrative computational pipeline for biomarker discovery using LC-MS-based comparative proteomic analysis. This platform consists of several computational tools for: (i) detecting peptides in the collected patterns; (ii) matching detected peptides across a number of LC-MS datasets and (iii) selecting discriminatory peptides between classes of samples.


AVAILABILITY
MS-BID source codes, binaries and documentations are freely available under LGPL from http://tools.proteomecenter.org/msBID.php.
5382663
Corra: Computational framework and tools for LC-MS discovery and targeted mass spectrometry-based proteomics
BackgroundQuantitative proteomics holds great promise for identifying proteins that are differentially abundant between populations representing different physiological or disease states. A range of computational tools is now available for both isotopically labeled and label-free liquid chromatography mass spectrometry (LC-MS) based quantitative proteomics. However, they are generally not comparable to each other in terms of functionality, user interfaces, information input/output, and do not readily facilitate appropriate statistical data analysis. These limitations, along with the array of choices, present a daunting prospect for biologists, and other researchers not trained in bioinformatics, who wish to use LC-MS-based quantitative proteomics.ResultsWe have developed Corra, a computational framework and tools for discovery-based LC-MS proteomics. Corra extends and adapts existing algorithms used for LC-MS-based proteomics, and statistical algorithms, originally developed for microarray data analyses, appropriate for LC-MS data analysis. Corra also adapts software engineering technologies (e.g. Google Web Toolkit, distributed processing) so that computationally intense data processing and statistical analyses can run on a remote server, while the user controls and manages the process from their own computer via a simple web interface. Corra also allows the user to output significantly differentially abundant LC-MS-detected peptide features in a form compatible with subsequent sequence identification via tandem mass spectrometry (MS/MS). We present two case studies to illustrate the application of Corra to commonly performed LC-MS-based biological workflows: a pilot biomarker discovery study of glycoproteins isolated from human plasma samples relevant to type 2 diabetes, and a study in yeast to identify in vivo targets of the protein kinase Ark1 via phosphopeptide profiling.ConclusionThe Corra computational framework leverages computational innovation to enable biologists or other researchers to process, analyze and visualize LC-MS data with what would otherwise be a complex and not user-friendly suite of tools. Corra enables appropriate statistical analyses, with controlled false-discovery rates, ultimately to inform subsequent targeted identification of differentially abundant peptides by MS/MS. For the user not trained in bioinformatics, Corra represents a complete, customizable, free and open source computational platform enabling LC-MS-based proteomic workflows, and as such, addresses an unmet need in the LC-MS proteomics field.
16464983
Self-Protection Model Based on Intelligent Agent
In this paper, we propose a self-protection model based on distributed intelligent agents. The major feather of this model is that autonomic agents can manage themselves given high-level objectives from administers. System can detect attacks and compromise, and recovering from security incidents without human intervention. We describe the model and the existing prototype, as well as some design and implementation experiences. Meanwhile several topics including structure of agent, relationship among agents and the establishment of a trustworthy system are discussed. We are implementing this model on the Eclipse Platform using Java. According to the experiments, this model provides significant advantages in scalability, flexibility, fault tolerance, and resistance to compromise.
36066337
H2 Control design for fuzzy dynamic systems via LMI
This paper addresses the robust H_2 control design of complex nonlinear systems which can be represented by a fuzzy dynamic model. Based on a continuous Lyapunov function and a piecewise continuous Lyapunov function respectively, two kinds of new control design methods are proposed using Lyapunov stability theory, robust H_2 control theory and Linear Matrix Inequalities (LMI) techniques. It is shown that the closed loop fuzzy control system is asymptotically stable with a white noise attenuation. The robust H_2 fuzzy controller can be obtained by using the LMI techniques. An example is given to demonstrate the application of the proposed design methods.
62212730
Distributed supercomputing to achieve real-time representation and manipulation of complex scenes
Abstract Besides the ray tracing technique, the radiosity method is another major approach for global illumination modeling in the field of computer graphics. Since this method needs a huge amount of storage space (both memory and disk) and a long pre-computation cycle, it is not suitable to implement it on conventional workstations. Supercomputers seem to be necessary for such kind of graphics applications. However, some problems appear with the integration of supercomputers and workstations in a distributed computing environment, especially for graphics application. The major problems are: (i) unavailability of standard higher-level mechanisms for building distributed application; (ii) transparent access to supercomputers without remote login or file transfer sessions; (iii) computation balance to make the best use of the performance of supercomputers and workstations; (iv) data transfer/storage strategy between supercomputers and workstations; and (v) conflict between interaction and response time for graphics applications. In this paper we suggest solutions to the above mentioned problems based on the remote procedure call (RPC) and the client/server model, for the radiosity package implementation. After the introduction of the radiosity method, we concentrate on the integration between supercomputers and high-performance workstations. We also convey some general issues related to distributed computing and present the experience with our implementation, such as the application protocol definition, error recovery, code debugging etc. The radiosity package has been implemented at ZGDV in Darmstadt, F.R.G. in a network environment including a Multiflow TRACE 7/300 supercomputer connected to several VAX, SUN and HP workstations.
5798533
A Flexible Routing Decision Framework to Support Diversified Application-Level Data Types in MANETs
Owing to the technological advances in computing power and wireless communications, applications that used to be only accessible on desktop computers are increasingly run-able on wireless devices. As a result, a wireless device user may run several applications at the same time, and these different applications may generate different data types. In a mobile ad hoc network (MANET) environment, which is popular among wireless device users, different data types may impose different routing considerations and prefer using different routing criteria. To support diversified routing requirements and to achieve efficient routing for applications with different data types, this paper proposes a novel Flexible Routing Decision (FRD) framework. The framework supports several routing metric types in one platform. It allows users to weigh, rank and use different types of routing metrics to select different routes on an individual application basis. It is expected that this approach could help users to find a route that is most suited to their application requirements.
3386880
Statistical Learning Techniques for Costing XML Queries
Developing cost models for query optimization is significantly harder for XML queries than for traditional relational queries. The reason is that XML query operators are much more complex than relational operators such as table scans and joins. In this paper, we propose a new approach, called COMET, to modeling the cost of XML operators; to our knowledge, COMET is the first method ever proposed for addressing the XML query costing problem. As in relational cost estimation, COMET exploits a set of system catalog statistics that summarizes the XML data; the set of "simple path" statistics that we propose is new, and is well suited to the XML setting. Unlike the traditional approach, COMET uses a new statistical learning technique called "transform regression" instead of detailed analytical models to predict the overall cost. Besides rendering the cost estimation problem tractable for XML queries, COMET has the further advantage of enabling the query optimizer to be self-tuning, automatically adapting to changes over time in the query workload and in the system environment. We demonstrate COMET's feasibility by developing a cost model for the recently proposed XNAV navigational operator. Empirical studies with synthetic, benchmark, and real-world data sets show that COMET can quickly obtain accurate cost estimates for a variety of XML queries and data sets.