7641777
GBNet: Deciphering regulatory rules in the co-regulated genes using a Gibbs sampler enhanced Bayesian network approach
BackgroundCombinatorial regulation of transcription factors (TFs) is important in determining the complex gene expression patterns particularly in higher organisms. Deciphering regulatory rules between cooperative TFs is a critical step towards understanding the mechanisms of combinatorial regulation.ResultsWe present here a Bayesian network approach called GBNet to search for DNA motifs that may be cooperative in transcriptional regulation and the sequence constraints that these motifs may satisfy. We showed that GBNet outperformed the other available methods in the simulated and the yeast data. We also demonstrated the usefulness of GBNet on learning regulatory rules between YY1, a human TF, and its co-factors. Most of the rules learned by GBNet on YY1 and co-factors were supported by literature. In addition, a spacing constraint between YY1 and E2F was also supported by independent TF binding experiments.ConclusionWe thus conclude that GBNet is a useful tool for deciphering the "grammar" of transcriptional regulation.
31075540
Reducing multiclass cancer classification to binary by output coding and SVM
Multiclass cancer classification based on microarray data is presented. The binary classifiers used combine support vector machines with a generalized output-coding scheme. Different coding strategies, decoding functions and feature selection methods are incorporated and validated on two cancer datasets: GCM and ALL. Using random coding strategy and recursive feature elimination, the testing accuracy achieved is as high as 83% on GCM data with 14 classes. Comparing with other classification methods, our method is superior in classificatory performance.
62074016
Dimension Reduction-Based Penalized Logistic Regression for Cancer Classification Using Microarray Data
The use of penalized logistic regression for cancer classification using microarray expression data is presented. Two dimension reduction methods are respectively combined with the penalized logistic regression so that both the classification accuracy and computational speed are enhanced. Two other machine-learning methods, support vector machines and least-squares regression, have been chosen for comparison. It is shown that our methods have achieved at least equal or better results. They also have the advantage that the output probability can be explicitly given and the regression coefficients are easier to interpret. Several other aspects, such as the selection of penalty parameters and components, pertinent to the application of our methods for cancer classification are also discussed.
3239171
Nonlinear Kernel MSE Methods for Cancer Classification
Combination of kernel PLS (KPLS) and kernel SVD (KSVD) with minimum-squared-error (MSE) criteria has created new machine learning methods for cancer classification and has been successfully applied to seven publicly available cancer datasets. Besides the high accuracy of the new methods, very fast training speed is also obtained because the matrix inversion in the original MSE procedure is avoided. Although the KPLS-MSE and the KSVD-MSE methods have equivalent accuracies, the KPLS achieves the same results using significantly less but more qualitative components.
17699029
Spherical parameterization for 3D surface analysis in volumetric images
We propose a new spherical parameterization algorithm for general genus-zero surfaces that aims to control the area and length distortions at the same time. Prior work either considers only the length distortion or applies only to volumetric surfaces. Our algorithm starts from an initial parameterization, performs local and global smoothing steps alternately to improve the mapping quality, and converges to the solution. The local method minimizes the area distortion at a local submesh by solving a linear system as well as controls the length distortion. The global method attempts to reduce the area distortion by equalizing its distribution over the whole sphere. We perform experimental studies showing the effectiveness of the technique. Our new algorithm has many applications in medical image analysis, computer vision, graphics and multimedia databases.
60639655
Parallel simulation of chip-multiprocessor by using multi-threading
Multi-core on one chip becomes the current main trend of micro processors' architecture, and simulation plays an important role in evaluating new processor architectures. Traditional ways of sequential simulation for chip-multiprocessor (CMP) require lots of simulation time which can't be afford during processor design phase. Therefore, exploiting parallelism is necessary for speeding up the multi-core processor simulation. 
 
In this paper, we present a parallel CMP simulator, which is implemented by using POSIX threads, and also present the programming methodology for converting a sequential single core simulator to a parallel CMP simulator. Furthermore, the tradeoffs in parallel simulator implementation are also discussed. The experiments are carried out on a four-CPU dual core Xeon server. Under weak consistence order model, the parallel simulator achieves a speedup of 7.2X comparing with the sequential one.
17173257
DBTIM: An Advanced Hardware Assisted Full Virtualization Architecture
Previous full virtualization techniques are implemented in software without any hardware assist. In this paper, for the first time, we propose an advanced hardware assisted full virtualization architecture-Dynamic Binary Translation in DIMM (DBTIM). We integrate a reconfigurable dynamic binary translation chip into a DIMM. DBTIM could be inserted to the mainboard as a normal DIMM, hence easily making the host CPU virtualizable. We also implement a DBTIM prototype on FPGA. Furthermore, we propose an EDBTIM technique-Enhanced Dynamic Binary Translator in Memory, which integrates dynamic binary translator and memory on one SDRAM chip. We analyze our new approaches and compare them with other modern virtualization techniques. We demonstrate that the new techniques in this paper could provide full virtualization to all kinds of CPU architectures, including x86, with better virtualization capability, better software and hardware compatibility, better performance and lower overheads.
14116656
The design and implementation of a program development system based on rewriting method
In this paper, we present a program development system based on rewriting method with the goal of providing an powerful tool for automatic software development and rapid prototyping. New mechanisms for defining constrained types and optimal rules are introduced to functional programming languages to achieve strong expressiveness. The enhanced functional programming language is combined with an algebraic specification language. Thus, the design from specification to program can be supported, and the efficiency and flexibility of programming can also be improved. In this system, both static and dynamic techniques are used to deal with constrained type check. All function definitions, computation constraints and optimal rules are regarded as rewriting rules. In order to provide a strong support for a large group of TRSs which may be neither terminating nor orthogonal, we propose a method using structure measure to decide TRSs' confluences. Based on this method, we present a partial completion algorithm to generate a rewriting model from a specification. Depending on term rewriting system, the mixed language system computes with parallel outermost and needed reduction. We explain the principles and implementation techniques in detail, some examples are provided.
29817800
Mining Flexible Multiple-Level Association Rules in All Concept Hierarchies (Extended Abstract)
We introduce the problem of mining FML (flexible multiple-level) association rules in all concept hierarchies related to a set of user-interested database attributes, as interesting association rules among data items may occur at multiple levels of multiple relevant concept hierarchies. We present a complete classification of all FML rules and show that direct application of previous research can find only a small part of strong FML rules. We propose an efficient method to generate all strong FML rules in all concept hierarchies.