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PubMedDate not supplied

A computer-aided drug discovery system for chemistry teaching.

The Schools Malaria Project (http://emalaria.soton.ac.uk/) brings together school students with university researchers in the hunt for a new antimalaria drug. The design challenge being offered to students is to use a distributed drug search and selection system to design potential antimalaria drugs. The system is accessed via a Web interface. This e-science project displays the results of the trials in an accessible manner, giving students an opportunity for discussion and debate both with peers and with the university contacts. The project has been implemented by using distributed computing techniques, spreading computer load over a network of machines that cross institutional boundaries, forming a grid. This provides access to greater computing power and allows a much more complex and detailed formulation of the drug design problem to be tackled for research, teaching, and learning.

Animals↗
PubMed1997

Posttraumatic stress disorder, self- and interpersonal dysfunction among sexually retraumatized women.

This study assessed self and interpersonal dysfunction as well as posttraumatic stress disorder (PTSD) among three groups of women: women sexually assaulted in both childhood and adulthood, women sexually assaulted only in adulthood and women who were never assaulted. Rates of PTSD were high and equivalent in the two assault groups. However, retraumatized women were more likely to be alexithymic, show dissociation scores indicating risk for dissociative disorders, and to have attempted suicide compared to the other two groups, who did not differ from each other. Additionally, only the retraumatized women experienced clinically significant levels of interpersonal problems. The findings suggest that formulations more inclusive than PTSD are required to capture the psychological difficulties experienced by this population. Treatment implications are discussed.

Adult↗
PubMed2005

Mapping of orthologous genes in the context of biological pathways: An application of integer programming.

Mapping biological pathways across microbial genomes is a highly important technique in functional studies of biological systems. Existing methods mainly rely on sequence-based orthologous gene mapping, which often leads to suboptimal mapping results because sequence-similarity information alone does not contain sufficient information for accurate identification of orthology relationship. Here we present an algorithm for pathway mapping across microbial genomes. The algorithm takes into account both sequence similarity and genomic structure information such as operons and regulons. One basic premise of our approach is that a microbial pathway could generally be decomposed into a few operons or regulons. We formulated the pathway-mapping problem to map genes across genomes to maximize their sequence similarity under the constraint that the mapped genes be grouped into a few operons, preferably coregulated in the target genome. We have developed an integer-programming algorithm for solving this constrained optimization problem and implemented the algorithm as a computer software program, p-map. We have tested p-map on a number of known homologous pathways. We conclude that using genomic structure information as constraints could greatly improve the pathway-mapping accuracy over methods that use sequence-similarity information alone.

Algorithms↗
PubMed1998

Approaches to the automatic discovery of patterns in biosequences.

This paper surveys approaches to the discovery of patterns in biosequences and places these approaches within a formal framework that systematises the types of patterns and the discovery algorithms. Patterns with expressive power in the class of regular languages are considered, and a classification of pattern languages in this class is developed, covering the patterns that are the most frequently used in molecular bioinformatics. A formulation is given of the problem of the automatic discovery of such patterns from a set of sequences, and an analysis is presented of the ways in which an assessment can be made of the significance of the discovered patterns. It is shown that the problem is related to problems studied in the field of machine learning. The major part of this paper comprises a review of a number of existing methods developed to solve the problem and how these relate to each other, focusing on the algorithms underlying the approaches. A comparison is given of the algorithms, and examples are given of patterns that have been discovered using the different methods.

Algorithms↗
PubMed2003

Dynamic modeling of genetic networks using genetic algorithm and S-system.

MOTIVATION: The modeling of system dynamics of genetic networks, metabolic networks or signal transduction cascades from time-course data is formulated as a reverse-problem. Previous studies focused on the estimation of only network structures, and they were ineffective in inferring a network structure with feedback loops. We previously proposed a method to predict not only the network structure but also its dynamics using a Genetic Algorithm (GA) and an S-system formalism. However, it could predict only a small number of parameters and could rarely obtain essential structures. In this work, we propose a unified extension of the basic method. Notable improvements are as follows: (1) an additional term in its evaluation function that aims at eliminating futile parameters; (2) a crossover method called Simplex Crossover (SPX) to improve its optimization ability; and (3) a gradual optimization strategy to increase the number of predictable parameters. RESULTS: The proposed method is implemented as a C program called PEACE1 (Predictor by Evolutionary Algorithms and Canonical Equations 1). Its performance was compared with the basic method. The comparison showed that: (1) the convergence rate increased about 5-fold; (2) the optimization speed was raised about 1.5-fold; and (3) the number of predictable parameters was increased about 5-fold. Moreover, we successfully inferred the dynamics of a small genetic network constructed with 60 parameters for 5 network variables and feedback loops using only time-course data of gene expression.

Algorithms↗
PubMed2003

Reconstructing tumor genome architectures.

Although cancer progression is often associated with genome rearrangements, little is known about the detailed genomic architecture of tumor genomes. The attempt to reconstruct the genomic organization of a tumor genome recently resulted in the development of the End Sequence Profiling (ESP) technique, and the application of this technique to human MCF7 tumor cells. We formulate the ESP Genome Reconstruction Problem, and develop an algorithm to solve this problem in the case of sparse ESP data. We apply our algorithm to analyze human MCF7 tumor cells, and obtain the first reconstruction of the putative architecture of human MCF7 tumor genome. Our results assist in the ongoing ESP analysis of MCF7 tumors by suggesting additional ESP experiments for the completion of a reliable reconstruction of the MCF7 tumor genome, and by focusing BAC re-sequencing efforts.

Algorithms↗
PubMed2006

Computing the maximum similarity bi-clusters of gene expression data.

MOTIVATIONS: Bi-clustering is an important approach in microarray data analysis. The underlying bases for using bi-clustering in the analysis of gene expression data are (1) similar genes may exhibit similar behaviors only under a subset of conditions, not all conditions, (2) genes may participate in more than one function, resulting in one regulation pattern in one context and a different pattern in another. Using bi-clustering algorithms, one can obtain sets of genes that are co-regulated under subsets of conditions. RESULTS: We develop a polynomial time algorithm to find an optimal bi-cluster with the maximum similarity score. To our knowledge, this is the first formulation for bi-cluster problems that admits a polynomial time algorithm for optimal solutions. The algorithm works for a special case, where the bi-clusters are approximately squares. We then extend the algorithm to handle various kinds of other cases. Experiments on simulation data and real data show that the new algorithms outperform most of the existing methods in many cases. Our new algorithms have the following advantages: (1) no discretization procedure is required, (2) performs well for overlapping bi-clusters and (3) works well for additive bi-clusters. AVAILABILITY: The software is available at http://www.cs.cityu.edu.hk/~liuxw/msbe/help.html.

Algorithms↗
PubMed2002

eCodonOpt: a systematic computational framework for optimizing codon usage in directed evolution experiments.

We present a systematic computational framework, eCodonOpt, for designing parental DNA sequences for directed evolution experiments through codon usage optimization. Given a set of homologous parental proteins to be recombined at the DNA level, the optimal DNA sequences encoding these proteins are sought for a given diversity objective. We find that the free energy of annealing between the recombining DNA sequences is a much better descriptor of the extent of crossover formation than sequence identity. Three different diversity targets are investigated for the DNA shuffling protocol to showcase the utility of the eCodonOpt framework: (i) maximizing the average number of crossovers per recombined sequence; (ii) minimizing bias in family DNA shuffling so that each of the parental sequence pair contributes a similar number of crossovers to the library; and (iii) maximizing the relative frequency of crossovers in specific structural regions. Each one of these design challenges is formulated as a constrained optimization problem that utilizes 0-1 binary variables as on/off switches to model the selection of different codon choices for each residue position. Computational results suggest that many-fold improvements in the crossover frequency, location and specificity are possible, providing valuable insights for the engineering of directed evolution protocols.

Aldose-Ketose Isomerases↗
PubMed2004

Steady to unsteady dynamics of a vesicle in a flow.

We investigate the dynamics of a vesicle in a shear flow on the basis of the newly proposed advected field (AF) method [T. Biben and C. Misbah, Eur. Phys. J. E 67, 031908 (2003)]. We also solve the same problem with the boundary integral formulation for the sake of comparison. We find that the AF results presented previously overestimated the tumbling threshold due to the finite size of the membrane, inherent to the AF model. A comparison between the two methods shows that only in the sharp interface limit (extrapolating the results to a vanishing width) the AF method leads to accurate quantitative results. We extensively investigate the tank-treading to tumbling transition, and compare our numerical results to the theory of Keller and Skalak which assumes a fixed ellipsoidal shape for the vesicle. We show that this theory describes correctly the two regimes, at least in two dimensions, even for the quite elongated non-convex shapes corresponding to red blood cells (and therefore far from ellipsoidal), This theory is, however, not fully quantitative. Finally we investigate the effect of a confinement on the tank-treading to tumbling transition, and show that the tumbling regime becomes unfavorable in a capillary vessel, which should have strong effects on blood rheology in confined geometries.

Computer Simulation↗
PubMed2005

Enumeration of RNA structures by matrix models.

We enumerate the number of RNA contact structures according to their genus, i.e., the topological character of their pseudoknots. By using a recently proposed matrix model formulation for the RNA folding problem, we obtain exact results for the simple case of an RNA molecule with an infinitely flexible backbone, in which any arbitrary pair of bases is allowed. We analyze the distribution of the genus of pseudoknots as a function of the total number of nucleotides along the phosphate-sugar backbone.

Models, Chemical↗
PubMed1998

Speckle reduction and contrast enhancement of echocardiograms via multiscale nonlinear processing.

This paper presents an algorithm for speckle reduction and contrast enhancement of echocardiographic images. Within a framework of multiscale wavelet analysis, we apply wavelet shrinkage techniques to eliminate noise while preserving the sharpness of salient features. In addition, nonlinear processing of feature energy is carried out to enhance contrast within local structures and along object boundaries. We show that the algorithm is capable of not only reducing speckle, but also enhancing features of diagnostic importance, such as myocardial walls in two-dimensional echocardiograms obtained from the parasternal short-axis view. Shrinkage of wavelet coefficients via soft thresholding within finer levels of scale is carried out on coefficients of logarithmically transformed echocardiograms. Enhancement of echocardiographic features is accomplished via nonlinear stretching followed by hard thresholding of wavelet coefficients within selected (midrange) spatial-frequency levels of analysis. We formulate the denoising and enhancement problem, introduce a class of dyadic wavelets, and describe our implementation of a dyadic wavelet transform. Our approach for speckle reduction and contrast enhancement was shown to be less affected by pseudo-Gibbs phenomena. We show experimentally that this technique produced superior results both qualitatively and quantitatively when compared to results obtained from existing denoising methods alone. A study using a database of clinical echocardiographic images suggests that such denoising and enhancement may improve the overall consistency of expert observers to manually defined borders.

Algorithms↗
PubMed1999

Lagrangian speckle model and tissue-motion estimation--theory.

It is known that when a tissue is subjected to movements such as rotation, shearing, scaling, etc., changes in speckle patterns that result act as a noise source, often responsible for most of the displacement-estimate variance. From a modeling point of view, these changes can be thought of as resulting from two mechanisms: one is the motion of the speckles and the other, the alterations of their morphology. In this paper, we propose a new tissue-motion estimator to counteract these speckle decorrelation effects. The estimator is based on a Lagrangian description of the speckle motion. This description allows us to follow local characteristics of the speckle field as if they were a material property. This method leads to an analytical description of the decorrelation in a way which enables the derivation of an appropriate inverse filter for speckle restoration. The filter is appropriate for linear geometrical transformation of the scattering function (LT), i.e., a constant-strain region of interest (ROI). As the LT itself is a parameter of the filter, a tissue-motion estimator can be formulated as a nonlinear minimization problem, seeking the best match between the pre-tissue-motion image and a restored-speckle post-motion image. The method is tested, using simulated radio-frequency (RF) images of tissue undergoing axial shear.

Biomechanical Phenomena↗
PubMed1999

Microwave imaging using the finite-element method and a sensitivity analysis approach.

A method for reconstructing the constitutive parameters of two-dimensional (2-D) penetrable scatterers from scattered field measurements is presented. This method is based on the differential formulation of the forward scattering problem, which is solved by applying the finite-element method (FEM). Given a set of scattered field measurements, the objective is to minimize a cost function which consists of two terms. The first is the standard error term, which is related to the measurements and their estimates, while the second term, which is related to the Tikhonov regularization, is used to heal the ill posedness of the inverse problem. The iterative Polak-Ribière nonlinear conjugate gradient algorithm is applied to the minimization of the cost function. During each iteration of the algorithm, the direction of correction is computed by using a sensitivity analysis approach, which is carried out by an elaborate finite-element scheme. The adoption of the finite-element method results in sparse systems of equations, while the computational burden is further reduced by applying the adjoint state vector methodology. Finally, a microwave medical imaging application, which is related to the detection of proliferated bone marrow, is examined, while the robustness of the proposed technique in the presence of noise and for different regularization levels is investigated.

Bone Marrow↗
PubMed2005

Quantifying motion in video recordings of neonatal seizures by regularized optical flow methods.

This paper presents the development of regularized optical flow computation methods and an evaluation of their performance in the extraction of quantitative motion information from video recordings of neonatal seizures. A general formulation of optical flow computation is presented and a mathematical framework for the development of practical tools for computing optical flow is outlined. In addition, this paper proposes an alternative formulation of the optical flow problem that relies on a discrete approximation of a family of quadratic functionals. These regularized optical flow computation methods are used to extract motion strength signals from video recordings of neonatal seizures.

Algorithms↗
PubMed2006

A Markov model for blind image separation by a mean-field EM algorithm.

This paper deals with blind separation of images from noisy linear mixtures with unknown coefficients, formulated as a Bayesian estimation problem. This is a flexible framework, where any kind of prior knowledge about the source images and the mixing matrix can be accounted for. In particular, we describe local correlation within the individual images through the use of Markov random field (MRF) image models. These are naturally suited to express the joint pdf of the sources in a factorized form, so that the statistical independence requirements of most independent component analysis approaches to blind source separation are retained. Our model also includes edge variables to preserve intensity discontinuities. MRF models have been proved to be very efficient in many visual reconstruction problems, such as blind image restoration, and allow separation and edge detection to be performed simultaneously. We propose an expectation-maximization algorithm with the mean field approximation to derive a procedure for estimating the mixing matrix, the sources, and their edge maps. We tested this procedure on both synthetic and real images, in the fully blind case (i.e., no prior information on mixing is exploited) and found that a source model accounting for local autocorrelation is able to increase robustness against noise, even space variant. Furthermore, when the model closely fits the source characteristics, independence is no longer a strict requirement, and cross-correlated sources can be separated, as well.

Algorithms↗
PubMed2006

Gridding spot centers of smoothly distorted microarray images.

We use an optimization technique to accurately locate a distorted grid structure in a microarray image. By assuming that spot centers deviate smoothly from a checkerboard grid structure, we show that the process of gridding spot centers can be formulated as a constrained optimization problem. The constraint is equal to the variations of the transform parameter. We demonstrate the accuracy of our algorithm on two sets of microarray images. One set consists of some images from the Stanford Microarray Database; we compare our centers with those annotated in the Database. The other set consists of oligonucleotide images, and we compare our results with those obtained by GenePix Pro 5.0. Our experiments were performed completely automatically.

Algorithms↗
PubMed2006

Estimation of two-dimensional affine transformations through polar curve matching and its application to image mosaicking and remote-sensing data registration.

This paper presents a new and effective method for estimating two-dimensional affine transformations and its application to image registration. The method is based on matching polar curves obtained from the radial projections of the image energies, defined as the squared magnitudes of their Fourier transforms. Such matching is formulated as a simple minimization problem whose optimal solution is found with the Levenberg-Marquardt algorithm. The analysis of affine transformations in the frequency domain exploits the well-known property whereby the translational displacement in this domain can be factored out and separately estimated through phase correlation after the four remaining degrees of freedom of the affine warping have been determined. Another important contribution of this paper, emphasized through one example of image mosaicking and one example of remote sensing image registration, consists in showing that affine motion can be accurately estimated by applying our algorithm to the shapes of macrofeatures extracted from the images to register. The excellent performance of the algorithm is also shown through a synthetic example of motion estimation and its comparison with another standard registration technique.

Algorithms↗
PubMed2002

Integrated surface model optimization for freehand three-dimensional echocardiography.

The major obstacle of three-dimensional (3-D) echocardiography is that the ultrasound image quality is too low to reliably detect features locally. Almost all available surface-finding algorithms depend on decent quality boundaries to get satisfactory surface models. We formulate the surface model optimization problem in a Bayesian framework, such that the inference made about a surface model is based on the integration of both the low-level image evidence and the high-level prior shape knowledge through a pixel class prediction mechanism. We model the probability of pixel classes instead of making explicit decisions about them. Therefore, we avoid the unreliable edge detection or image segmentation problem and the pixel correspondence problem. An optimal surface model best explains the observed images such that the posterior probability of the surface model for the observed images is maximized. The pixel feature vector as the image evidence includes several parameters such as the smoothed grayscale value and the minimal second directional derivative. Statistically, we describe the feature vector by the pixel appearance probability model obtained by a nonparametric optimal quantization technique. Qualitatively, we display the imaging plane intersections of the optimized surface models together with those of the ground-truth surfaces reconstructed from manual delineations. Quantitatively, we measure the projection distance error between the optimized and the ground-truth surfaces. In our experiment, we use 20 studies to obtain the probability models offline. The prior shape knowledge is represented by a catalog of 86 left ventricle surface models. In another set of 25 test studies, the average epicardial and endocardial surface projection distance errors are 3.2 +/- 0.85 mm and 2.6 +/- 0.78 mm, respectively.

Algorithms↗