5469426
Unraveling condition specific gene transcriptional regulatory networks in Saccharomyces cerevisiae
BackgroundGene expression and transcription factor (TF) binding data have been used to reveal gene transcriptional regulatory networks. Existing knowledge of gene regulation can be presented using gene connectivity networks. However, these composite connectivity networks do not specify the range of biological conditions of the activity of each link in the network.ResultsWe present a novel method that utilizes the expression and binding patterns of the neighboring nodes of each link in existing experimentally-based, literature-derived gene transcriptional regulatory networks and extend them in silico using TF-gene binding motifs and a compendium of large expression data from Saccharomyces cerevisiae. Using this method, we predict several hundreds of new transcriptional regulatory TF-gene links, along with experimental conditions in which known and predicted links become active. This approach unravels new links in the yeast gene transcriptional regulatory network by utilizing the known transcriptional regulatory interactions, and is particularly useful for breaking down the composite transcriptional regulatory network to condition specific networks.ConclusionOur methods can facilitate future binding experiments, as they can considerably help focus on the TFs that must be surveyed to understand gene regulation.(Supplemental material and the latest version of the MATLAB implementation of the United Signature Algorithm is available online at [1] or [see Additional files 1, 2, 3, 4, 5, 6, 7, 8, 9, 10])
11702143
Adaptive nonlinear discriminant analysis by regularized minimum squared errors
Kernelized nonlinear extensions of Fisher's discriminant analysis, discriminant analysis based on generalized singular value decomposition (LDA/GSVD), and discriminant analysis based on the minimum squared error formulation (MSE) have recently been widely utilized for handling undersampled high-dimensional problems and nonlinearly separable data sets. As the data sets are modified from incorporating new data points and deleting obsolete data points, there is a need to develop efficient updating and downdating algorithms for these methods to avoid expensive recomputation of the solution from scratch. In this paper, an efficient algorithm for adaptive linear and nonlinear kernel discriminant analysis based on regularized MSE, called adaptive KDA/RMSE, is proposed. In adaptive KDA/RMSE, updating and downdating of the computationally expensive eigenvalue decomposition (EVD) or singular value decomposition (SVD) is approximated by updating and downdating of the QR decomposition achieving an order of magnitude speed up. This fast algorithm for adaptive kernelized discriminant analysis is designed by utilizing regularization techniques and the relationship between linear and nonlinear discriminant analysis and the MSE. In addition, an efficient algorithm to compute leave-one-out cross validation is also introduced by utilizing downdating of KDA/RMSE.
8166541
Sparse non-negative matrix factorizations via alternating non-negativity-constrained least squares for microarray data analysis
MOTIVATION
Many practical pattern recognition problems require non-negativity constraints. For example, pixels in digital images and chemical concentrations in bioinformatics are non-negative. Sparse non-negative matrix factorizations (NMFs) are useful when the degree of sparseness in the non-negative basis matrix or the non-negative coefficient matrix in an NMF needs to be controlled in approximating high-dimensional data in a lower dimensional space.


RESULTS
In this article, we introduce a novel formulation of sparse NMF and show how the new formulation leads to a convergent sparse NMF algorithm via alternating non-negativity-constrained least squares. We apply our sparse NMF algorithm to cancer-class discovery and gene expression data analysis and offer biological analysis of the results obtained. Our experimental results illustrate that the proposed sparse NMF algorithm often achieves better clustering performance with shorter computing time compared to other existing NMF algorithms.


AVAILABILITY
The software is available as supplementary material.
16407614
Requirements-based dynamic metrics in object-oriented systems
Because early design decisions can have a major long term impact on the performance of a system, early evaluation of the high-level architecture can be an important risk mitigation technique. This paper proposes a technique for predicting the volume of data that will flow across a network in a distributed system. The prediction is based upon anticipated execution of scenarios and can be applied at an extremely early stage of the design. It is driven by requirements specifications and captures dynamic metrics by defining typical usage patterns in terms of scenarios. Scenarios are then mapped to architectural components, and dataflow across inter-partition links is estimated. The feasibility of the approach is demonstrated through an experiment in which predicted metrics are compared to runtime measurements.
1551402
Diverge-Merge Processor (DMP): Dynamic Predicated Execution of Complex Control-Flow Graphs Based on Frequently Executed Paths
This paper proposes a new processor architecture for handling hard-to-predict branches, the diverge-merge processor (DMP). The goal of this paradigm is to eliminate branch mispredictions due to hard-to-predict dynamic branches by dynamically predicating them without requiring ISA support for predicate registers and predicated instructions. To achieve this without incurring large hardware cost and complexity, the compiler provides control-flow information by hints and the processor dynamically predicates instructions only on frequently executed program paths. The key insight behind DMP is that most control-flow graphs look and behave like simple hammock (if-else) structures when only frequently executed paths in the graphs are considered. Therefore, DMP can dynamically predicate a much larger set of branches than simple hammock branches. Our evaluations show that DMP out performs a baseline processor with an aggressive branch predictor by 19.3% on average over SPEC integer 95 and 2000 benchmarks, through a reduction of 38% in pipeline flushes due to branch mispredictions, while consuming 9.0% less energy. We also compare DMP with previously proposed predication and dual-path/multipath execution paradigms in terms of performance, complexity, and energy consumption, and find that DMP is the highest performance and also the most energy-efficient design
15509328
New features for affine-invariant shape classification
An object seen from different viewpoints results in differently deformed images. Affine-invariant shape classification must classify correctly the object, regardless its viewpoint. In this paper, we propose new local and global features invariant under affine transformation. These features can be used for supervised or unsupervised shape classification, and for shape-based image indexing and retrieval. One of the proposed features is related to the convex deficiency and the others are extracted from the area matrix. Area matrix was used by Shen for the similarity matching in image retrieval. However, differently from the Shen's work, we parameterize the shape contour using the affine-length parameter. This makes our features robust to affine parameterization, while Shen's work does not have this property. Experimental results indicate that our method can classify correctly even highly deformed and noisy shapes using small training sets.
36946020
Face Representation Method Using Pixel-to-Vertex Map (PVM) for 3D Model Based Face Recognition
3D model based approach for face recognition has been spotlighted as a robust solution under variant conditions of pose and illumination. Since a generative 3D face model consists of a large number of vertices, a 3D model based face recognition system is generally inefficient in computation time. In this paper, we propose a novel 3D face representation algorithm to reduce the number of vertices and optimize its computation time. Finally, we evaluate the performance of proposed algorithm with the Korean face database collected using a stereo-camera based 3D face capturing device.
14113310
Techniques for improved placement-coupled logic replication
Several recent papers have utilized logic replication driven by placement-level timing analysis for improving clock period (e.g., [1], [8], [18], and [2]). All of these papers demonstrated, through various optimization strategies, the potential of the basic technique of replication. In this paper we propose a number of techniques aimed at more fully realizing this potential within the framework employed in [8]. As reported in [7], there are situations in which the approach of [8] fails to yield significant additional improvement due largely to the effects of reconvergence in the netlist. We suggest the use of rectilinear Steiner arborescence embedding as a tool for overcoming this limitation. We also propose techniques for fanout partitioning and cell relocation which are cognizant of both wirelength and timing impact for improved solution quality. We report the effect of other techniques including new replication cost computation, lower-bounding of achievable clock period, and wirelength estimation. We have implemented and experimented with these techniques in FPGA domain. In many cases we were able to approach a fixed flip-flop lower-bound on achievable clock period. Promising experimental results are reported with average 17.4% (up to 39.9%) delay reduction compared with the timing-driven placement from VPR[16] and average 9.3% (up to 37.2%) reduction compared with the basic fanin tree embedder from [8].