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At least 73 records · Page 4Linked to original sources
PubMed2006

A coherent computational approach to model bottom-up visual attention.

Visual attention is a mechanism which filters out redundant visual information and detects the most relevant parts of our visual field. Automatic determination of the most visually relevant areas would be useful in many applications such as image and video coding, watermarking, video browsing, and quality assessment. Many research groups are currently investigating computational modeling of the visual attention system. The first published computational models have been based on some basic and well-understood Human Visual System (HVS) properties. These models feature a single perceptual layer that simulates only one aspect of the visual system. More recent models integrate complex features of the HVS and simulate hierarchical perceptual representation of the visual input. The bottom-up mechanism is the most occurring feature found in modern models. This mechanism refers to involuntary attention (i.e., salient spatial visual features that effortlessly or involuntary attract our attention). This paper presents a coherent computational approach to the modeling of the bottom-up visual attention. This model is mainly based on the current understanding of the HVS behavior. Contrast sensitivity functions, perceptual decomposition, visual masking, and center-surround interactions are some of the features implemented in this model. The performances of this algorithm are assessed by using natural images and experimental measurements from an eye-tracking system. Two adequate well-known metrics (correlation coefficient and Kullbacl-Leibler divergence) are used to validate this model. A further metric is also defined. The results from this model are finally compared to those from a reference bottom-up model.

Artificial Intelligence↗
PubMed2006

Computational approaches to gene prediction.

The problems associated with gene identification and the prediction of gene structure in DNA sequences have been the focus of increased attention over the past few years with the recent acquisition by large-scale sequencing projects of an immense amount of genome data. A variety of prediction programs have been developed in order to address these problems. This paper presents a review of the computational approaches and gene-finders used commonly for gene prediction in eukaryotic genomes. Two approaches, in general, have been adopted for this purpose: similarity-based and ab initio techniques. The information gleaned from these methods is then combined via a variety of algorithms, including Dynamic Programming (DP) or the Hidden Markov Model (HMM), and then used for gene prediction from the genomic sequences.

Animals↗
PubMed2006

Computational approaches for predicting CYP-related metabolism properties in the screening of new drugs.

The site of biotransformation, the extent and rate of metabolism and the number of active metabolic pathways are among the most important characteristics of the pharmacokinetics of a drug. The catalytic activity of drug metabolizing enzymes is likely the most influential determinant of the pharmacokinetic variability. Metabolic stability is the prerequisite for sustaining the therapeutically relevant concentrations. Metabolic inhibition and induction can give rise to clinically important drug-drug interactions. A variety of computational approaches are currently available for predicting different cytochrome P450 (CYP)-related metabolism endpoints. The present review will describe these approaches and their impact on drug development process. Indications on the available software for the implementation will also be given.

Animals↗
PubMed2003

Binding modes of noncompetitive AMPA antagonists: a computational approach.

The activity of functional AMPA receptors (AMPARs) is modulated by noncompetitive antagonists. So far, no information about the molecular mechanism of action and the localization of the binding pocket(s) is available. We speculated that the leucine/isoleucine/valine binding protein (LIVBP)-like domain of AMPAR, localized at the extracellular N-terminus of the receptor, might be involved in the binding of noncompetitive antagonists and we tested this hypothesis through a computational approach involving the comparison with NMDA and metabotropic glutamate receptors and the generation of a 3D homology model of the LIVBP-like domain of AMPAR. The results suggest that the interdomain cleft of the LIVBP-like domain of AMPAR may contain the noncompetitive antagonist binding pocket.

Amino Acid Sequence↗
PubMed2000

Three-dimensional quantitative structure activity relationship computational approaches for prediction of human in vitro intrinsic clearance.

Future alternatives to the presently accepted in vitro paradigm of prediction of intrinsic clearance, which could be used earlier in the drug discovery process, would potentially accelerate efforts to identify better drug candidates with more favorable metabolic profiles and less likelihood of failure with regard to human pharmacokinetic attributes. In this study we describe two computational methods for modeling human microsomal and hepatocyte intrinsic clearance data derived from our laboratory and the literature, which utilize pharmacophore features or descriptors derived from molecular structure. Human microsomal intrinsic clearance data generated for 26 known therapeutic drugs were used to build computational models using commercially available software (Catalyst and Cerius(2)), after first converting the data to hepatocyte intrinsic clearance. The best Catalyst pharmacophore model gave an r of 0.77 for the observed versus predicted clearance. This pharmacophore was described by one hydrogen bond acceptor, two hydrophobic features, and one ring aromatic feature essential to discriminate between high and low intrinsic clearance. The Cerius(2) quantitative structure activity relationship (QSAR) model gave an r(2) = 0.68 for the observed versus predicted clearance and a cross-validated r(2) (q(2)) of 0.42. Similarly, literature data for human hepatocyte intrinsic clearance for 18 therapeutic drugs were also used to generate two separate models using the same computational approaches. The best Catalyst pharmacophore model gave an improved r of 0.87 and was described by two hydrogen bond acceptors, one hydrophobe, and 1 positive ionizable feature. The Cerius(2) QSAR gave an r(2) of 0.88 and a q(2) of 0.79. Each of these models was then used as a test set for prediction of the intrinsic clearance data in the other data set, with variable successes. These present models represent a preliminary application of QSAR software to modeling and prediction of human in vitro intrinsic clearance.

Catalysis↗
PubMed2006

Computational approaches for predicting protein-protein interactions: a survey.

Discovery of the protein interactions that take place within a cell can provide a starting point for understanding biological regulatory pathways. Global interaction patterns among proteins, for example, can suggest new drug targets and aid the design of new drugs by providing a clearer picture of the biological pathways in the neighborhoods of the drug targets. High-throughput experimental screens have been developed to detect protein-protein interactions, however, they show high rates of errors in terms of false positives and false negatives. Many computational approaches have been proposed to tackle the problem of protein-protein interaction prediction. They range from comparative genomics based methods to data integration based approaches. Challenging properties of protein-protein interaction data have to be addressed appropriately before a higher quality interaction map with better coverage can be achieved. This paper presents a survey of major works in computational prediction of protein-protein interactions, explaining their assumptions, main ideas, and limitations.

Humans↗
PubMed1999

Treatment planning for brachytherapy: an integer programming model, two computational approaches and experiments with permanent prostate implant planning.

An integer linear programming model is proposed as a framework for optimizing seed placement and dose distribution in brachytherapy treatment planning. The basic model involves using 0/1 indicator variables to describe the placement or non-placement of seeds in a prespecified three-dimensional grid of potential locations. The dose delivered to each point in a discretized representation of the diseased organ and neighbouring healthy tissue can then be modelled as a linear combination of the indicator variables. A system of linear constraints is imposed to attempt to keep the dose level at each point to within specified target bounds. Since it is physically impossible to satisfy all constraints simultaneously, each constraint uses a variable to either record when the target dose level is achieved, or to record the deviation from the desired level. These additional variables are embedded into an objective function to be optimized. Variations on this model are discussed and two computational approaches--a branch-and-bound algorithm and a genetic algorithm--for finding 'optimal' seed placements are described. Results of computational experiments on a collection of prostate cancer cases are reported. The results indicate that both optimization algorithms are capable of producing good solutions within 5 to 15 min, and that small variations in model parameters can have a measurable effect on the dose distribution of the resulting plans.

Algorithms↗
PubMed2005

Inferring haplotypes at the NAT2 locus: the computational approach.

BACKGROUND: Numerous studies have attempted to relate genetic polymorphisms within the N-acetyltransferase 2 gene (NAT2) to interindividual differences in response to drugs or in disease susceptibility. However, genotyping of individuals single-nucleotide polymorphisms (SNPs) alone may not always provide enough information to reach these goals. It is important to link SNPs in terms of haplotypes which carry more information about the genotype-phenotype relationship. Special analytical techniques have been designed to unequivocally determine the allocation of mutations to either DNA strand. However, molecular haplotyping methods are labour-intensive and expensive and do not appear to be good candidates for routine clinical applications. A cheap and relatively straightforward alternative is the use of computational algorithms. The objective of this study was to assess the performance of the computational approach in NAT2 haplotype reconstruction from phase-unknown genotype data, for population samples of various ethnic origin. RESULTS: We empirically evaluated the effectiveness of four haplotyping algorithms in predicting haplotype phases at NAT2, by comparing the results with those directly obtained through molecular haplotyping. All computational methods provided remarkably accurate and reliable estimates for NAT2 haplotype frequencies and individual haplotype phases. The Bayesian algorithm implemented in the PHASE program performed the best. CONCLUSION: This investigation provides a solid basis for the confident and rational use of computational methods which appear to be a good alternative to infer haplotype phases in the particular case of the NAT2 gene, where there is near complete linkage disequilibrium between polymorphic markers.

Algorithms↗
PubMed2001

A new computational approach for cortical imaging.

Estimation of current or potential distribution on the cortex is used to obtain information about neural sources from the scalp recorded electroencephalogram. If the active sources in the brain are superficial, the estimated field distribution on the cortex also yields information about the active source configuration. In these cases, these methods can be used as source localization methods. In this study, we concentrate on finite-element-based cortex potential estimation. Usually these methods require surface interpolation of the recorded voltages at the electrodes onto the entire scalp surface. We propose a new computational approach which does not require the use of surface interpolation but does it implicitly and uses only the recorded data at the electrodes. We refer to this method as the systematic approach (SA). We compare the SA with the surface interpolation approach (IA) and show that the SA is able to produce somewhat better accuracy than the IA. However, the main asset is that the sensitivity of the cortical potential maps to the regularization parameter is significantly lower than with the IA.

Brain Mapping↗
PubMed2001

Computational approaches towards the rational design of drug-like compound libraries.

During the practice of combinatorial chemistry, it has been realized that molecular diversity is not the only essential feature in a synthetically feasible library. In addition, it is of utmost importance to enrich potential libraries with those molecules which could be converted to viable drug candidates. Given the enormous number of potentially synthesizable compounds, there is a need to design a subset of true "drug-like" compounds. In addition, a paradigm shift in drug discovery has resulted in the integration of pharmacokinetic and drug development activities into early stages of lead discovery. In particular, in silico filters are being developed and used to help identify and screen out compounds that are unlikely to become drugs. This paper highlights recent computational approaches towards the design of drug-like compound libraries, in particular, the prediction of drug-likeness in a more general sense as well as intestinal absorption through passive transport, the permeation of the blood-brain barrier and recent developments towards identification of potentially metabolically unstable molecules. Current computational tools for library design allow the incorporation of medicinal chemistry knowledge into library planning by a variety of methods, ranging from the use of privileged building blocks and simple counting of structural properties (e.g. number of hydrogen bonding partners) to relatively complex regression or neural network-based models to explain oral bioavailability and other pharmacokinetic properties by structural features. These tools are being incorporated more frequently into drug design according to the "rule-of-five" which refers to simple descriptors correlated to oral drug absorption. Combining experimental knowledge with effective computational filtering and prediction of various aspects of drug-likeness thus facilitates the rapid and cost-effective elimination of poor candidates prior to synthesis and helps focus attention on interesting molecules.

Animals↗
PubMed1996

Historical height samples with shortfall: a computational approach.

Research in economic history frequently uses human height as a proxy for net nutrition. This anthropometric method enables historians to measure time trends and differences in nutritional status. However, the most widely used data sources for historical heights, military mustering registers, cannot be regarded as random samples of the underlying population. The lower side of the otherwise normal distribution is eroded by a phenomenon called shortfall, because shorter individuals are under-represented below a certain threshold (truncation point). This paper reviews two widely used methods for analyzing historical height samples with shortfall - the Quantile Bend Estimator (QBE) and the Reduced Sample Maximum Likelihood Estimator (RSMLE). Because of the drawbacks of these procedures, a new computational approach for identifying the truncation point of height samples with shortfall, using density estimation techniques, is proposed and illustrated on an Austrian dataset. Finally, this procedure, combined with a truncated regression model, is compared to the QBE to estimate the mean and the standard deviation. The results demonstrate the deficiencies of the QBE again and cast a good light on the new method.

Austria↗
PubMed2004

Computational approaches for the analysis of gene neighbourhoods in prokaryotic genomes.

Gene order in prokaryotes is conserved to a much lesser extent than protein sequences. Only some operons, primarily those that encode physically interacting proteins, are conserved in all or most of the bacterial and archaeal genomes. Nevertheless, even the limited conservation of operon organisation that is observed provides valuable evolutionary and functional clues through multiple genome comparisons. With the rapid growth in the number and diversity of sequenced prokaryotic genomes, functional inferences for uncharacterized genes located in the same conserved gene neighborhood with well-studied genes are becoming increasingly important. In this review, we discuss various computational approaches for identification of conserved gene strings and construction of local alignments of gene orders in prokaryotic genomes.

Algorithms↗
PubMed2006

Goals and means in action observation: a computational approach.

Many of our daily activities are supported by behavioural goals that guide the selection of actions, which allow us to reach these goals effectively. Goals are considered to be important for action observation since they allow the observer to copy the goal of the action without the need to use the exact same means. The importance of being able to use different action means becomes evident when the observer and observed actor have different bodies (robots and humans) or bodily measurements (parents and children), or when the environments of actor and observer differ substantially (when an obstacle is present or absent in either environment). A selective focus on the action goals instead of the action means furthermore circumvents the need to consider the vantage point of the actor, which is consistent with recent findings that people prefer to represent the actions of others from their own individual perspective. In this paper, we use a computational approach to investigate how knowledge about action goals and means are used in action observation. We hypothesise that in action observation human agents are primarily interested in identifying the goals of the observed actor's behaviour. Behavioural cues (e.g. the way an object is grasped) may help to disambiguate the goal of the actor (e.g. whether a cup is grasped for drinking or handing it over). Recent advances in cognitive neuroscience are cited in support of the model's architecture.

Attention↗
PubMed1997

Aphasic sentence comprehension as a resource deficit: a computational approach.

This article describes a new computational model of aphasic sentence comprehension. The model is based on the premise that all aphasics, however different, share a common deficit which determines a considerable amount of the individual variation observed in their sentence comprehension performance. This common deficit is construed as a pathological reduction in the activation resources of a working memory system that subserves sentence comprehension (Miyake, Carpenter, & Just, 1994). To test the theoretical feasibility of the resource reduction hypothesis, a new computer model of aphasic sentence comprehension was developed and tested. We describe the model as well as some initial simulation results, indicating that the model can account for some of the sentence complexity and severity effects that have been reported in the aphasia literature.

Aphasia↗
PubMed2006

A computational approach to animal breeding.

We propose a computational model of mating strategies for controlled animal breeding programs. A mating strategy in a controlled breeding program is a heuristic with some optimization criteria as a goal. Thus, it is appropriate to use the computational tools available for analysis of optimization heuristics. In this paper, we propose the first discrete model of the controlled animal breeding problem and analyse heuristics for two possible objectives: (1) breeding for maximum diversity and (2) breeding a target individual. These two goals are representative of conservation biology and agricultural livestock management, respectively. We evaluate several mating strategies and provide upper and lower bounds for the expected number of matings. While the population parameters may vary and can change the actual number of matings for a particular strategy, the order of magnitude of the number of expected matings and the relative competitiveness of the mating heuristics remains the same. Thus, our simple discrete model of the animal breeding problem provides a novel viable and robust approach to designing and comparing breeding strategies in captive populations.

Algorithms↗