SEARCH · PubMed Health

Results for “Parallel Algorithms”

Explore indexed PubMed citations for clinical trials, systematic reviews and public health research. Read source abstracts and follow each citation to its original PubMed record.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 649 records · Page 36Linked to original sources

PubMed1999

A study of deleterious gene structure in plants using Markov chain Monte Carlo.

The characteristics of deleterious genes have been of great interest in both theory and practice in genetics. Because of the complex genetic mechanism of these deleterious genes, most current studies try to estimate the overall magnitude of mortality effects on a population, which is characterized classically by the number of lethal equivalents. This number is a combination of several parameters, each of which has a distinct biological effect on genetic mortality. In conservation and breeding programs, it is important to be able to distinguish among different combinations of these parameters that lead to the same number of lethal equivalents, such as a large number of mildly deleterious genes or a few lethal genes, The ability to distinguish such parameter combinations requires more than one generation of mating. We propose a model for survival data from a two-generation mating experiment on the plant species Brassica rapa, and we enable inference with Markov chain Monte Carlo. This computational strategy is effective because a vast amount of missing genotype information must be accounted for. In addition to the lethal equivalents, the two-generation data provide separate information on the average intensity of mortality and the average number of deleterious genes carried by an individual. In our Markov chain Monte Carlo algorithm, we use a vector proposal distribution to overcome inefficiency of a single-site Gibbs sampler. Information about environmental effects is obtained from an outcrossing experiment conducted in parallel with the two-generation mating experiments.

Algorithms↗

PubMed2005

Segmentation of microscopic images of small intestinal glands with directional 2-D filters.

We present an image segmentation algorithm for small intestinal glands consisting of goblet cells that are evenly distributed and arranged in parallel at the base. Making use of the properties of the chain distribution of the goblet cells, directional 2-dimensional (2-D) linear filters with different orientations were designed to enhance the rims of the intestinal glands. Segmentations are based on the combined responses of the multiple zero-phase directional 2-D linear filters. For comparisons, outputs of combined directional filters are shown along with those of the comparable nondirectional Gaussian filters. Segmentation results of small intestinal glands of both normal and cancer cases are provided.

Algorithms↗

PubMed1998

Detection of suspected malignant patterns in three-dimensional magnetic resonance breast images.

In this article, a Boolean Neural Network (BNN) is used for the detection of suspected malignant regions in 3D breast magnetic resonance (MR) images. The BNN is characterized by fast learning and classification, guaranteed convergence, and simple, integer weight calculations. The BNN learning algorithm is incremental, which allows the addition and deletion of training patterns without unlearning those already learned. The incremental learning algorithm automatically reduces the training set and trains the network only with those examples estimated to be useful. The architecture is suitable for parallel hardware implementation using available Very Large Scale Integration (VLSI) technology. The BNN was trained by using a set of malignant, benign, and false-positive patterns, extracted by experts, from selected MR studies, by using an incremental learning algorithm. After training, the network was tested by means of a consistency checking test, cross validation techniques, and patterns from actual MR breast images. During the consistency test, the BNN was tested by using the same patterns used for training. The BNN classification accuracy in this case was 99.75%, proving the ability of the BNN to select useful patterns from the training set. Then, a leave one out cross-validation (LOOCV) test was done by using patterns from the training set and the classification accuracy was 90%. Next, an extended training set was created by shifting the original patterns in different directions. A cross-validation test was then performed by dividing the set of patterns into a training and a test set. Classification accuracy was compared to the nearest neighbor classifier. Results showed that the BNN achieved an average of 77% classification accuracy while requiring only 34% of the original training set. On the other hand, the nearest neighbor classifier achieved an accuracy of 57.9% while retaining the whole training set. Another test using actual MR slices different from the training set was done and results compared favorably to a radiologist's findings. Test results show the BNN's capability to detect suspected malignant regions in 3D MR images of the breast. The proposed BNN architecture can save the radiologist a great deal of time browsing MR slices searching for suspected malignancies.

Algorithms↗

PubMed2005

Air-coupled through-transmission fan-beam tomography using divergent capacitive ultrasonic transducers.

Abstracttrasonic transducers (CUTs) with curved backplates was used to acquire signals through regions of air containing solid objects, air flow, and temperature fields. Fan-beam datasets were collected and used in a tomographic reconstruction algorithm to produce cross-sectional images of the area under interrogation. In the case of the solid objects, occluded rays from the projections were accounted for using a compensation algorithm and a priori knowledge of the object. A rebinning routine was used to pick out parallel ray sets from the fan-beam data. The effects of further reducing the number of datasets also were investigated, and, in the case of imaging solid objects, characteristic Gibbs phenomena were seen in the reconstructions as expected. However, when imaging temperature and flow fields, the aliasing artefacts were not seen, but the reconstructed values decreased with the size of dataset used. The effect of changing the kernel filter function also was investigated, with the different filters giving the best compromise between image noise, reconstruction accuracy, and amount of data required in each scenario.

Algorithms↗

PubMed2003

Analysis of gene expression profiles: an application of memetic algorithms to the minimum sum-of-squares clustering problem.

Microarrays have become a key technology in experimental molecular biology since they allow monitoring of gene expression for more than 10,000 genes in parallel producing huge amounts of data. In the exploration of transcriptional regulatory networks, an important task is to cluster gene expression data to identify groups of genes with similar patterns and hence similar function. In this paper, memetic algorithms (MAs)-evolutionary algorithms incorporating local search-are proposed for minimum sum-of-squares clustering (MSSC). In a fitness landscape analysis, it is shown that the MSSC problem has correlation structure exploitable by MAs. The proposed MAs are shown to be superior to multi-start k-means as well as five other clustering algorithms from the bioinformatics literature including hierarchical algorithms and self-organizing maps. Although the fitness values of the different clustering solutions lie close together, it is shown that the solutions differ significantly from each other in terms of cluster memberships which is extremely important for the biological interpretation of the clustering results.

Algorithms↗

PubMedDate not supplied

Humoral sensitization against rejected grafts: specific antibodies to graft immunogenic amino acid triplets.

Humoral sensitization against immunogenic amino acid (aa) triplets expressed on a rejected graft was analyzed in 83 retransplant candidates. All patients had lost a graft with HLA-A,-B mismatches. The alloantibodies were detected by a complement-dependent cytotoxicity (CDC) technique and an ELISA method in parallel; they were classified as HLA graft-specific (GS) and non-GS antibodies. The aa triplet specificity of the antibodies was assessed using the HLAMatchmaker algorithm. HLA class I antibodies were detected in 74 of 78 (94%) cases, including GS reactivity in 55 (74.3%) and non-GS in 72 (97.2%), either alone (n = 19) or in parallel with GS antibodies (n = 53). For all HLA-GS-antibody-reactive patients, we defined the specificity against immunogenic aa triplets on the previous graft. Moreover, antibodies specific to graft aa triplets were observed within the non-GS antibodies among 19 of 19 and 28 of 53 cases, respectively. Therefore, aa triplet-specific antibodies against the rejected graft were present in all 74 cases with HLA class I antibodies. Antibodies against aa triplets expressed on all HLA class I-mismatched graft antigens were present in 73% of cases. The high extent of humoral alloreactivity against a rejected graft supports the decision to avoid repeated exposure to immunogenic aa triplet mismatches on a second graft. An accurate analysis for performed antibodies in these cases may be beneficial to select the most suitable second donor.

Antibody Formation↗

PubMed1993

Faster sequential genetic linkage computations.

Linkage analysis using maximum-likelihood estimation is a powerful tool for locating genes. As available data sets have grown, the computation required for analysis has grown exponentially and become a significant impediment. Others have previously shown that parallel computation is applicable to linkage analysis and can yield order-of-magnitude improvements in speed. In this paper, we demonstrate that algorithmic modifications can also yield order-of-magnitude improvements, and sometimes much more. Using the software package LINKAGE, we describe a variety of algorithmic improvements that we have implemented, demonstrating both how these techniques are applied and their power. Experiments show that these improvements speed up the programs by an order of magnitude, on problems of moderate and large size. All improvements were made only in the combinatorial part of the code, without restoring to parallel computers. These improvements synthesize biological principles with computer science techniques, to effectively restructure the time-consuming computations in genetic linkage analysis.

Algorithms↗

PubMed2005

Automatic selection of parameters for vessel/neurite segmentation algorithms.

An automated method is presented for selecting optimal parameter settings for vessel/neurite segmentation algorithms using the minimum description length principle and a recursive random search algorithm. It trades off a probabilistic measure of image-content coverage against its conciseness. It enables nonexpert users to select parameter settings objectively, without knowledge of underlying algorithms, broadening the applicability of the segmentation algorithm, and delivering higher morphometric accuracy. It enables adaptation of parameters across batches of images. It simplifies the user interface to just one optional parameter and reduces the cost of technical support. Finally, the method is modular, extensible, and amenable to parallel computation. The method is applied to 223 images of human retinas and cultured neurons, from four different sources, using a single segmentation algorithm with eight parameters. Improvements in segmentation quality compared to default settings using 1000 iterations ranged from 4.7%-21%. Paired t-tests showed that improvements are statistically significant (p < 0.0005). Most of the improvement occurred in the first 44 iterations. Improvements in description lengths and agreement with the ground truth were strongly correlated (p = 0.78).

Algorithms↗

PubMed1997

Secondary structure computer prediction of the poliovirus 5' non-coding region is improved by a genetic algorithm.

Comparison of the secondary structure of the 5' non-coding region of poliovirus 3 RNA derived from the genetic algorithm with the model of Skinner et al. (J. Mol. Biol., 207, 379-392, 1989) demonstrates many of the confirmed structural elements. The genetic algorithm (Shapiro and Navetta, J. Supercomput., 8, 195-201, 1994) generates a population of all possible stems, then mixes, combines, and recombines these stems in multiple iterations on a massively parallel computer, ultimately selecting a most fit structure based on its energy. The secondary structure of the region containing the determinants of neurovirulence was better predicted using the genetic algorithm, whereas the dynamic programming algorithm (Zuker, Science, 244, 48-52, 1989) required phylogenetic comparative sequence analysis to arrive at the correct conclusion. In addition, artificial mutations were introduced throughout this region of the genome and although rearrangements in structure may occur, many structures persisted, suggesting that the given structures thus selected may have evolved to withstand isolated mutations. The genetic algorithm-derived structure for the 5' non-coding region compares favorably with the biological data and functions previously described, and contains all of the 'persistent' structures, suggesting also that the persistence factor may be an aid to validating structures.

Algorithms↗

PubMed1995

Supercomputer algorithms for efficient linear octree encoding of three-dimensional brain images.

We designed and implemented algorithms for three-dimensional (3-D) reconstruction of brain images from serial sections using two important supercomputer architectures, vector and parallel. These architectures were represented by the Cray YMP and Connection Machine CM-2, respectively. The programs operated on linear octree representations of the brain data sets, and achieved 500-800 times acceleration when compared with a conventional laboratory workstation. As the need for higher resolution data sets increases, supercomputer algorithms may offer a means of performing 3-D reconstruction well above current experimental limits.

Algorithms↗

PubMed1991

[Response analysis for an approximate 3-D image reconstruction in cone-beam SPECT].

Cone-beam Single Photon Emission Computed Tomography (SPECT) offers the potential for a large increase in sensitivity as compared with parallel hole or fan-beam collimation. Three-dimensional image reconstruction was approximately accomplished by backprojecting filtered projections using a two-dimensional fan-beam algorithm. The cone-beam projection data were formed from mathematical phantoms as analytically derived line integrals of the density. In order to reduce the processing time, the filtered projections were backprojected into each planes parallel to the circle on which the focal point moved. Discrepancy of source position and degradation of resolution were investigated by computer simulation in three-dimensional image space. The obtained results suggest that, the nearer to the central plane or the axis of rotation, the less image degradation is performed. By introducing a parameter of angular difference between the focal point and fixed point in the image space during rotation, degradation of the reconstructed image can be estimated for any cone-beam SPECT system.

Algorithms↗

PubMed2001

[Genetic algorithms and its application to spectral analysis].

Genetic algorithm derived from the principle of natural selection and the concepts of genetics is a global search method, which is not only highly effective but also parallel. Its essential theory, operating method, application to spectral analysis and trend of development are reviewed with 66 references.

Algorithms↗

PubMed2002

YIN, a fundamental frequency estimator for speech and music.

An algorithm is presented for the estimation of the fundamental frequency (F0) of speech or musical sounds. It is based on the well-known autocorrelation method with a number of modifications that combine to prevent errors. The algorithm has several desirable features. Error rates are about three times lower than the best competing methods, as evaluated over a database of speech recorded together with a laryngograph signal. There is no upper limit on the frequency search range, so the algorithm is suited for high-pitched voices and music. The algorithm is relatively simple and may be implemented efficiently and with low latency, and it involves few parameters that must be tuned. It is based on a signal model (periodic signal) that may be extended in several ways to handle various forms of aperiodicity that occur in particular applications. Finally, interesting parallels may be drawn with models of auditory processing.

Algorithms↗

PubMed1987

Optimization by stimulating molecular evolution.

Based on the analogy between mathematical optimization and molecular evolution and on Eigen's quasi-species model of molecular evolution, an evolutionary algorithm for combinatorial optimization has been developed. This algorithm consists of a versatile variation scheme and an innovative decision rule, the essence of which lies in a radical revision of the conventional philosophy of optimization: A number of configurations of variables with better values, instead of only a single best configuration, are selected as starting points for the next iteration. As a result the search proceeds in parallel along a number of routes and is unlikely to get trapped in local optima. An important innovation of the algorithm is introduction of a constraint to let the starting points always keep a certain distance from each other so that the search is able to cover a larger region of space effectively. The main advantage of the algorithm is that it has more chances to find the global optimum and as many local optima as possible in a single run. This has been demonstrated in preliminary computational experiments.

Algorithms↗

PubMed2000

A real-time volumetric visualization system for electrical impedance tomography.

Three dimensional (3D) electrical impedance tomography (EIT) presents many additional challenges over and above those associated with two dimensional EIT systems. With present two dimensional (2D) systems, tomographs can be reconstructed and displayed on a PC with a standard computer monitor. In addition, using appropriate data acquisition hardware and simple image reconstruction algorithms, it is possible to collect, reconstruct and display volumetric EIT images in real time using parallel processing architectures. The advantages of this 'real-time' capability are many and include the ability to immediately assess the correct functioning of the system and the ability to track patient events and the effect of procedures in real time. Whilst 3D EIT boundary datasets can be collected in real time, their real-time image reconstruction and display presents some computational challenges. This explains why, to date, no real-time solutions have been presented. In addition the use of a standard computer monitor to display 3D volumes is unsatisfactory since not all depth cues are preserved when using this type of 2D display device. We present a system which is capable of displaying 3D EIT datasets in real time and allows interactive modification of the user's viewpoint. This allows the user to fly around (and through) the EIT volumetric dataset.

Algorithms↗

PubMed1996

Automated particle classification based on digital acquisition and analysis of flow cytometric pulse waveforms.

In flow cytometry, the typical use of front-end analog processing limits the pulse waveform features that can be measured to pulse integral, height, and width. Direct digitizing of the waveforms provides a means for the extraction of additional features, for example, pulse skewness and kurtosis, and Fourier properties. In this work, we have first demonstrated that the Fourier properties of the pulse can be employed usefully for discrimination between different types of cells that otherwise cannot be classified by using only time-domain features of the pulse. We then implemented and evaluated automatic procedures for cell classification based on neural networks. We established that neural networks could provide an efficient means of classification of cell types without the need for user interaction. The neural networks were also employed in an innovative manner for analysis of the digital flow cytometric data without feature extraction. The performance of the neural networks was compared with that of a more conventional means of classification, the K-means clustering algorithm. Neural networks can be realized in hardware, and this, in addition to their highly parallel architecture, makes them an important potential part of real-time analysis systems. These results are discussed in terms of the design of a real-time digital data acquisition system for flow cytometry.

Animals↗

PubMed1993

Quantitative analysis of gel electrophoretograms by image analysis and least squares modeling.

A computer-aided quantitative method for a complex analysis of gel electrophoretograms is presented. The analysis consists of several steps: (i) determination of the background image by methods of mathematical morphology and its subtraction from the gel image, (ii) selection of an appropriate part of the gel lane including curved lanes and lanes with a nonuniform width, (iii) computation of the lane densitogram by averaging several lane-parallel scans, (iv) decomposition of the lane densitogram into component bands using a data selecting algorithm and Marquardt's minimizer. Several different functions for component bands are utilized. It is shown that the densitogram can be decomposed into component bands with reasonable accuracy only if an appropriate model function is chosen. The algorithms are tested on several different gel electrophoretograms which show typical features as a nonuniform background, curved lanes, an asymmetrical band shape and a superposition of small bands on the shoulders of big ones. It is shown that overlapped bands are best approximated by an asymmetrical Gausian curve and an asymmetrical Gauss-Cauchy function. Linear response to the serial dilution of the protein sample is tested.

DNA, Bacterial↗

PubMed2003

Rigid-body dynamics in the isothermal-isobaric ensemble: a test on the accuracy and computational efficiency.

We have developed a time-reversible rigid-body (rRB) molecular dynamics algorithm in the isothermal-isobaric (NPT) ensemble. The algorithm is an extension of rigid-body dynamics [Matubayasi and Nakahara, J Chem Phys 1999, 110, 3291] to the NPT ensemble on the basis of non-Hamiltonian statistical mechanics [Martyna, G. J. et al., J Chem Phys 1994, 101, 4177]. A series of MD simulations of water as well as fully hydrated lipid bilayer systems have been undertaken to investigate the accuracy and efficiency of the algorithm. The rRB algorithm was shown to be superior to the state-of-the-art constraint-dynamics algorithm SHAKE/RATTLE/ROLL, with respect to computational efficiency. However, it was revealed that both algorithms produced accurate trajectories of molecules in the NPT as well as NVT ensembles, as long as a reasonably short time step was used. A couple of multiple time-step (MTS) integration schemes were also examined. The advantage of the rRB algorithm for computational efficiency increased when the MD simulation was carried out using MTS on parallel processing computer systems; total computer time for MTS-MD of a lipid bilayer using 64 processors was reduced by about 40% using rRB instead of SHAKE/RATTLE/ROLL.

Journal Article↗