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PubMed2005

Computer prediction of drug resistance mutations in proteins.

Drug resistance is of increasing concern in the treatment of infectious diseases and cancer. Mutation in drug-interacting disease proteins is one of the primary causes for resistance particularly against anti-infectious drugs. Prediction of resistance mutations in these proteins is valuable both for the molecular dissection of drug resistance mechanisms and for predicting features that guide the design of new agents to counter resistant strains. Several protein structure- and sequence-based computer methods have been explored for mechanistic study and prediction of resistance mutations. These methods and their usefulness are reviewed here.

Computer Simulation↗
PubMed1999

Measurement of electrical current density distribution in a simple head phantom with magnetic resonance imaging.

Knowledge of the influence of the human skull on the electrical current (d.c.) distribution within the brain tissue could prove useful in measuring impedance changes inside the human head. These changes can be related to physiological functions. The studies presented in this paper examine the current density distribution in a simple phantom consisting of a saline filled tank (to simulate scalp and brain) and a ring made of dental grade plaster of Paris (to simulate the human skull). Images of the distribution of the d.c. density of the phantom with and without the plaster of Paris ring were produced using a magnetic resonance imaging technique. These images indicate that the skull is likely to produce a more uniform d.c. density within the brain.

Brain↗
PubMed2004

A parallel framework for the FE-based simulation of knee joint motion.

We present an object-oriented framework for the finite-element (FE)-based simulation of the human knee joint motion. The FE model of the knee joint is acquired from the patients in vivo by using magnetic resonance imaging. The MRI images are converted into a three-dimensional model and finally an all-hexahedral mesh for the FE analysis is generated. The simulation environment uses nonlinear finite-element analysis (FEA) and is capable of handling contact of the model to handle the complex rolling/sliding motion of the knee joint. The software strictly follows object-oriented concepts of software engineering in order to guarantee maximum extensibility and maintainability. The final goal of this work-in-progress is the creation of a computer-based biomechanical model of the knee joint which can be used in a variety of applications, ranging from prosthesis design and treatment planning (e.g., optimal reconstruction of ruptured ligaments) over surgical simulation to impact computations in crashworthiness simulations.

Computer Simulation↗
PubMed2002

Remote processing server for ECG-based clinical diagnosis support.

In this paper, we present the development of a remote server that provides a user-friendly access to advanced electrocardiographic (ECG) signal processing techniques. The prototype supplies telemedicine facilities to doctors for clinical indexes remote computation to support diagnosis through the Internet. The user-friendly interface is based on the selection of the desired ECG signal processing tools on a Web browser window. The centralized structure of the system permits unique and user-independent update and management of the software and, therefore, is especially suitable for remote or rural regions to have access to the new ECG information techniques.

Algorithms↗
PubMed2004

Fusing images with different focuses using support vector machines.

Many vision-related processing tasks, such as edge detection, image segmentation and stereo matching, can be performed more easily when all objects in the scene are in good focus. However, in practice, this may not be always feasible as optical lenses, especially those with long focal lengths, only have a limited depth of field. One common approach to recover an everywhere-in-focus image is to use wavelet-based image fusion. First, several source images with different focuses of the same scene are taken and processed with the discrete wavelet transform (DWT). Among these wavelet decompositions, the wavelet coefficient with the largest magnitude is selected at each pixel location. Finally, the fused image can be recovered by performing the inverse DWT. In this paper, we improve this fusion procedure by applying the discrete wavelet frame transform (DWFT) and the support vector machines (SVM). Unlike DWT, DWFT yields a translation-invariant signal representation. Using features extracted from the DWFT coefficients, a SVM is trained to select the source image that has the best focus at each pixel location, and the corresponding DWFT coefficients are then incorporated into the composite wavelet representation. Experimental results show that the proposed method outperforms the traditional approach both visually and quantitatively.

Algorithms↗
PubMed2005

Large margin nearest neighbor classifiers.

The nearest neighbor technique is a simple and appealing approach to addressing classification problems. It relies on the assumption of locally constant class conditional probabilities. This assumption becomes invalid in high dimensions with a finite number of examples due to the curse of dimensionality. Severe bias can be introduced under these conditions when using the nearest neighbor rule. The employment of a locally adaptive metric becomes crucial in order to keep class conditional probabilities close to uniform, thereby minimizing the bias of estimates. We propose a technique that computes a locally flexible metric by means of support vector machines (SVMs). The decision function constructed by SVMs is used to determine the most discriminant direction in a neighborhood around the query. Such a direction provides a local feature weighting scheme. We formally show that our method increases the margin in the weighted space where classification takes place. Moreover, our method has the important advantage of online computational efficiency over competing locally adaptive techniques for nearest neighbor classification. We demonstrate the efficacy of our method using both real and simulated data.

Algorithms↗
PubMed2006

Training reformulated radial basis function neural networks capable of identifying uncertainty in data classification.

This paper introduces a learning algorithm that can be used for training reformulated radial basis function neural networks (RBFNNs) capable of identifying uncertainty in data classification. This learning algorithm trains a special class of reformulated RBFNNs, known as cosine RBFNNs, by updating selected adjustable parameters to minimize the class-conditional variances at the outputs of their radial basis functions (RBFs). The experiments verify that quantum neural networks (QNNs) and cosine RBFNNs trained by the proposed learning algorithm are capable of identifying uncertainty in data classification, a property that is not shared by cosine RBFNNs trained by the original learning algorithm and conventional feed-forward neural networks (FFNNs). Finally, this study leads to a simple classification strategy that can be used to improve the classification accuracy of QNNs and cosine RBFNNs by rejecting ambiguous feature vectors based on their responses.

Algorithms↗
PubMed1999

A unifying review of linear gaussian models.

Factor analysis, principal component analysis, mixtures of gaussian clusters, vector quantization, Kalman filter models, and hidden Markov models can all be unified as variations of unsupervised learning under a single basic generative model. This is achieved by collecting together disparate observations and derivations made by many previous authors and introducing a new way of linking discrete and continuous state models using a simple nonlinearity. Through the use of other nonlinearities, we show how independent component analysis is also a variation of the same basic generative model. We show that factor analysis and mixtures of gaussians can be implemented in autoencoder neural networks and learned using squared error plus the same regularization term. We introduce a new model for static data, known as sensible principal component analysis, as well as a novel concept of spatially adaptive observation noise. We also review some of the literature involving global and local mixtures of the basic models and provide pseudocode for inference and learning for all the basic models.

Algorithms↗
PubMedDate not supplied

An efficient method for parallel CRC automatic generation.

The State Transition Equation (STE) based method to automatically generate the parallel CRC circuits for any generator polynomial or required amount of parallelism is presented. The parallel CRC circuit so generated is partially optimized before being fed to synthesis tools and works properly in our LAN transceiver. Compared with the cascading method, the proposed method gives better timing results and significantly reduces the synthesis time, in particular.

Computer Communication Networks↗
PubMed2004

Optimal parallel algorithm for shortest paths problem on interval graphs.

This paper presents an efficient parallel algorithm for the shortest-path problem in interval graph for computing shortest-paths in a weighted interval graph that runs in O(n) time with n intervals in a graph. A linear processor CRCW algorithm for determining the shortest-paths in an interval graphs is given.

Algorithms↗
PubMedDate not supplied

Imaging-based dynamic liver testing--a computerized simulation.

BACKGROUND/AIMS: The mechanisms for maintaining liver function are complex and currently used liver function tests give limited and often nonspecific and insensitive results. The aim of the study was to demonstrate a dynamic liver test with a tomographic imaging modality as sampling method by means of a computer simulation. METHODOLOGY: A virtual three-dimensional liver model, consisting of parenchyma and bile ducts, was created using computer aided design/computer aided manufacturing software. An intravenously administered, bile excreted test substance was simulated in the model by awarding changing densities to the parenchyma and bile ducts in 12 identical sets of the model. RESULTS: Imaging-based density measurements enabled the creation of time-density curves reflecting the transport of the simulated test substance in the parenchymal mass and bile ducts. By means of quantitative analysis of the curves, parenchymal uptake and excretion and intrahepatic bile flow could be assessed independently. CONCLUSIONS: The method enables comparison of function in different liver segments and may have particular value in investigating diseases that affect the liver in a non-homogeneous fashion, for example primary sclerosing cholangitis. Imaging sampling can theoretically be applied with any tomographic imaging technique for which a suitable test substrate exists, including computed tomography, magnetic resonance imaging or single photon emission tomography.

Bile↗
PubMedDate not supplied

A novel multipurpose Excel tool for equilibrium speciation based on Newton-Raphson method and on a hybrid genetic algorithm.

A new algorithm for simulation of chemical equilibria is developed, based on classical Newton-Raphson method applied to mass balance. This tool, named EST (Equilibrium Speciation Tool), is improved by using a robust Genetic Algorithm. In addition, EST works by using Excel spreadsheets and therefore offers the innovation of a great simplicity and versatility. In fact, it allows the users to simulate, or to obtain from experimental data, desired chemical-physical parameters as well as to interact with other available or freely created Excel tools. The reliability of this utility is here proved by comparison with some published data by other authors, concerning both complicated homogeneous and heterogeneous equilibria. In addiction its flexibility is tested computing thermodynamic parameters by using experimental calorimetric data referred to the complex formation of cobalt(II) with a macrocyclic ligand. A brief review and comparison of the relative robustness and quickness of main numerical methods are also reported.

Algorithms↗
PubMed2004

Is optimal solution of every NP-complete or NP-hard problem determined from its characteristic for DNA-based computing.

Cook's Theorem [Cormen, T.H., Leiserson, C.E., Rivest, R.L., 2001. Introduction to Algorithms, second ed., The MIT Press; Garey, M.R., Johnson, D.S., 1979. Computer and Intractability, Freeman, San Fransico, CA] is that if one algorithm for an NP-complete or an NP-hard problem will be developed, then other problems will be solved by means of reduction to that problem. Cook's Theorem has been demonstrated to be correct in a general digital electronic computer. In this paper, we first propose a DNA algorithm for solving the vertex-cover problem. Then, we demonstrate that if the size of a reduced NP-complete or NP-hard problem is equal to or less than that of the vertex-cover problem, then the proposed algorithm can be directly used for solving the reduced NP-complete or NP-hard problem and Cook's Theorem is correct on DNA-based computing. Otherwise, a new DNA algorithm for optimal solution of a reduced NP-complete problem or a reduced NP-hard problem should be developed from the characteristic of NP-complete problems or NP-hard problems.

Algorithms↗
PubMed2005

Phase-constrained parallel MR image reconstruction.

A generalized method for phase-constrained parallel MR image reconstruction is presented that combines and extends the concepts of partial-Fourier reconstruction and parallel imaging. It provides a framework for reconstructing images employing either or both techniques and for comparing image quality achieved by varying k-space sampling schemes. The method can be used as a parallel image reconstruction with a partial-Fourier reconstruction built in. It can also be used with trajectories not readily handled by straightforward combinations of partial-Fourier and SENSE-like parallel reconstructions, including variable-density, and non-Cartesian trajectories. The phase constraint specifies a better-conditioned inverse problem compared to unconstrained parallel MR reconstruction alone. This phase-constrained parallel MRI reconstruction offers a one-step alternative to the standard combination of homodyne and SENSE reconstructions with the added benefit of flexibility of sampling trajectory. The theory of the phase-constrained approach is outlined, and its calibration requirements and limitations are discussed. Simulations, phantom experiments, and in vivo experiments are presented.

Algorithms↗
PubMed2005

Computer technology in detection and staging of prostate carcinoma: a review.

After two decades of increasing interest and research activity, computer-assisted diagnostic approaches are reaching the stage where more routine deployment in clinical practice is becoming a possibility [Kruppinski, E.A., 2004. Computer-aided detection in clinical environment: Benefits and challenges for radiologists. Radiology 231, 7-9]. This is particularly the case in the analysis of mammographic images [Helvie, M.A., Hadjiiski, L., Makariou, E., Chan, H.P., Petrick, N., Sahiner, B., Lo, S.C., Freedman, M., Adler, D., Bailey, J., Blane, C., Hoff, D., Hunt, K., Joynt, L., Klein, K., Paramagul, C., Patterson, S.K., Roubidoux, M.A., 2004. Sensitivity of noncommercial computer-aided detection system for mammographic breast cancer detection: pilot clinical trial. Radiology 231, 208-214] and in the detection of pulmonary nodules [Reeves, A.P., Kostis, W.J., 2000. Computer-aided diagnosis for lung cancer. Radiol. Clin. North Am. 38, 497-509]. However, similar approaches can be applied more widely with the promise of increasing clinical utility in other areas. We review how computer-aided approaches may be applied in the diagnosis and staging of prostatic cancer. The current status of computer technology is reviewed, covering artificial neural networks for detection and staging, computerised biopsy simulation and computer-assisted analysis of ultrasound and magnetic resonance images.

Adenocarcinoma↗
PubMed1997

Distributed system for processing 3D medical images.

Three-dimensional (3D) image data generated by radiological imaging modalities such as CT and MRI can provide detailed structural insight. Automating the analysis of these images can improve the consistency of the results and reduce user interaction time, but introduces a tremendous computational burden. To address this problem, we have designed a distributed processing environment for the rapid processing of 3D medical images. Our system allows a user to perform automatic 3D filtering, segmentation, and measurement on a 3D image using a heterogeneous network of processors and the PVM protocol.

Computer Communication Networks↗
PubMedDate not supplied

Similarity searching in databases of flexible 3D structures using autocorrelation vectors derived from smoothed bounded distance matrices.

This paper presents an exploratory study of a novel method for flexible 3-D similarity searching based on autocorrelation vectors and smoothed bounded distance matrices. Although the new approach is unable to outperform an existing 2-D similarity searching in terms of enrichment factors, it is able to retrieve different compounds at a given percentage of the hit-list and so may be a useful adjunct to other similarity searching methods.

Computing Methodologies↗