6104018
A framework for modelling virus gene expression data
Short, high-dimensional, Multivariate Time Series (MTS) data are common in many fields such as medicine, finance and science, and any advance in modelling this kind of data would be beneficial. Nowhere is this truer than functional genomics where effective ways of analysing gene expression data are urgently needed. Progress in this area could help obtain a “global” view of biological processes, and ultimately lead to a great improvement in the quality of human life. We present a computational framework for modelling this type of data, and report experimental results of applying this framework to the analysis of gene expression data in the virology domain. The framework contains a three-step modelling strategy: correlation search, variable grouping, and short MTS modelling. Novel research is involved in each step which has been individually tested on different real-world datasets in engineering and medicine. This is the first attempt to integrate all these components into a coherent computational framework, and test the framework on a very challenging application area, producing promising results.
5117413
Consensus clustering and functional interpretation of gene-expression data
Microarray analysis using clustering algorithms can suffer from lack of inter-method consistency in assigning related gene-expression profiles to clusters. Obtaining a consensus set of clusters from a number of clustering methods should improve confidence in gene-expression analysis. Here we introduce consensus clustering, which provides such an advantage. When coupled with a statistically based gene functional analysis, our method allowed the identification of novel genes regulated by NFκB and the unfolded protein response in certain B-cell lymphomas.
9772572
A spatio-temporal Bayesian network classifier for understanding visual field deterioration
OBJECTIVE
Progressive loss of the field of vision is characteristic of a number of eye diseases such as glaucoma which is a leading cause of irreversible blindness in the world. Recently, there has been an explosion in the amount of data being stored on patients who suffer from visual deterioration including field test data, retinal image data and patient demographic data. However, there has been relatively little work in modelling the spatial and temporal relationships common to such data. In this paper we introduce a novel method for classifying visual field (VF) data that explicitly models these spatial and temporal relationships.


METHODOLOGY
We carry out an analysis of our proposed spatio-temporal Bayesian classifier and compare it to a number of classifiers from the machine learning and statistical communities. These are all tested on two datasets of VF and clinical data. We investigate the receiver operating characteristics curves, the resulting network structures and also make use of existing anatomical knowledge of the eye in order to validate the discovered models.


RESULTS
Results are very encouraging showing that our classifiers are comparable to existing statistical models whilst also facilitating the understanding of underlying spatial and temporal relationships within VF data. The results reveal the potential of using such models for knowledge discovery within ophthalmic databases, such as networks reflecting the 'nasal step', an early indicator of the onset of glaucoma.


CONCLUSION
The results outlined in this paper pave the way for a substantial program of study involving many other spatial and temporal datasets, including retinal image and clinical data.
2422545
Probe-level measurement error improves accuracy in detecting differential gene expression
MOTIVATION
Finding differentially expressed genes is a fundamental objective of a microarray experiment. Numerous methods have been proposed to perform this task. Existing methods are based on point estimates of gene expression level obtained from each microarray experiment. This approach discards potentially useful information about measurement error that can be obtained from an appropriate probe-level analysis. Probabilistic probe-level models can be used to measure gene expression and also provide a level of uncertainty in this measurement. This probe-level measurement error provides useful information which can help in the identification of differentially expressed genes.


RESULTS
We propose a Bayesian method to include probe-level measurement error into the detection of differentially expressed genes from replicated experiments. A variational approximation is used for efficient parameter estimation. We compare this approximation with MAP and MCMC parameter estimation in terms of computational efficiency and accuracy. The method is used to calculate the probability of positive log-ratio (PPLR) of expression levels between conditions. Using the measurements from a recently developed Affymetrix probe-level model, multi-mgMOS, we test PPLR on a spike-in dataset and a mouse time-course dataset. Results show that the inclusion of probe-level measurement error improves accuracy in detecting differential gene expression.


AVAILABILITY
The MAP approximation and variational inference described in this paper have been implemented in an R package pplr. The MCMC method is implemented in Matlab. Both software are available from http://umber.sbs.man.ac.uk/resources/puma.
17082553
HyPE: hybrid power estimation for IP-based systems-on-chip
In this paper, we present a novel power estimation scheme for programmable systems consisting of predesigned datapath and memory components. The proposed hybrid methodology yields highly accurate estimates within short runtimes by combining high-level simulation with analytical macromodeling of circuit characteristics. The kernel of our methodology is a simulation-free power estimation scheme for memoryless datapaths comprising several IP blocks connected in fixed topologies. The outer shell of our hybrid scheme is a functional simulation, which is performed only on the interfaces between memoryless components and memory blocks. This simulation accurately captures the control signals that affect the flow of data and, consequently, the utilization and power dissipation of hardware. Experimental results validate the accuracy and efficiency of our methodology. We applied our static power estimation kernel to signal processing and data encryption datapaths. For designs of up to 576 IP blocks, the average error of our power estimates is 7.3% in comparison with switch-level simulation results. We implemented our hybrid scheme into a power estimation tool, called HYPE, and used it to explore various architectural alternatives in the design of a 256-state Viterbi decoder and a Rijndael encryptor. For designs with about 1 million transistors, our estimator terminates within seconds. Compared with commercial state-of-the-art gate-level power estimators, our proposed methodology is up to 1000 times faster with 5.4% deviation on average.
1644440
Design of a high-throughput low-power IS95 Viterbi decoder
The design of high-throughput large-state Viterbi decoders relies on the use of multiple arithmetic units. The global communication channels among these parallel processors often consist of long interconnect wires, resulting in large area and high power consumption. In this paper, we propose a data-transfer oriented design methodology to implement a low-power 256-state rate-1/3 IS95 Viterbi decoder. Our architectural level scheme uses operation partitioning, packing, and scheduling to analyze and optimize interconnect effects in early design stages. In comparison with other published Viterbi decoders, our approach reduces the global data transfers by up to 75% and decreases the amount of global buses by up to 48%, while enabling the use of deeply pipelined datapaths with no data forwarding. In the RTL implementation of the individual processors, we apply precomputation in conjunction with saturation arithmetic to further reduce power dissipation with provably no coding performance degradation. Designed using a 0.25 μ standard cell library, our decoder achieves a throughput of 20 Mbps in simulation and dissipates only 450 mW.
33701205
Inverse dynamics and simulation of a 3-DOF spatial parallel manipulator
Recently the parallel manipulators with less DOF have attracted the researchers, but works on their dynamics are relative few. In this paper, an inverse dynamic formulation is presented by the Newton-Euler approach for a spatial parallel manipulator, which has two translational degrees of freedom and one rotational degree of freedom. The inverse kinematics analysis is firstly performed in closed form. Then the force and moment equilibrium equations for the manipulator are presented. According to the kinematic constraints of the legs and the platform, some joint constraint forces are eliminated and an algorithm to solve the actuator forces is given. In addition, ADAMS is used to perform the kinematic and dynamic simulation for the manipulator. The simulation results are compared to those derived from algebraic formulae and the comparison shows the validity of the mathematical model.
39271056
A hybrid representation of environment models in image-based real time walkthrough
In this paper, a hybrid representation of environment models using a combination of points and polygons is proposed. Through the model, a real time walkthrough of a complex environment can be achieved. We start from multiple depth reference images and classify the pixels of images into two categories, corresponding respectively to planar and non-planar surfaces in 3D space, then all the redundant points are removed by a comparison algorithm of sampling rate. For pixels corresponding to nonplanar surfaces, the point representation is maintained, and a local reconstruction and resampling process is employed to re-sample a set of points that distribute more uniformly on the surfaces. The re-sampled points are organized by an OBB-tree according to their space coordinates and a multiresolution structure is built to improve the rendering efficiency. For pixels corresponding to planar surfaces, the plane coefficients are reconstructed and all their appearances in the reference images are merged to texture maps. Under this hybrid representation, texture mapping and point-based rendering are employed to render the novel views, to take full advantage of the acceleration utility of graphics hardware. The algorithm demonstrates the combined advantages of approaches in traditional computer graphics and PBR/IBR, texture mapping for plane surfaces and point-based rendering for high detail surfaces and shapes.