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PubMed2006

A fast coarse filtering method for peptide identification by mass spectrometry.

MOTIVATION: We reformulate the problem of comparing mass-spectra by mapping spectra to a vector space model. Our search method leverages a metric space indexing algorithm to produce an initial candidate set, which can be followed by any fine ranking scheme. RESULTS: We consider three distance measures integrated into a multi-vantage point index structure. Of these, a semi-metric fuzzy-cosine distance using peptide precursor mass constraints performs the best. The index acts as a coarse, lossless filter with respect to the SEQUEST and ProFound scoring schemes, reducing the number of distance computations and returned candidates for fine filtering to about 0.5% and 0.02% of the database respectively. The fuzzy cosine distance term improves specificity over a peptide precursor mass filter, reducing the number of returned candidates by an order of magnitude. Run time measurements suggest proportional speedups in overall search times. Using an implementation of ProFound's Bayesian score as an example of a fine filter on a test set of Escherichia coli protein fragmentation spectra, the top results of our sample system are consistent with that of SEQUEST.

Algorithms↗
PubMed1997

Detection of convergent and parallel evolution at the amino acid sequence level.

Adaptive evolution at the molecular level can be studied by detecting convergent and parallel evolution at the amino acid sequence level. For a set of homologous protein sequences, the ancestral amino acids at all interior nodes of the phylogenetic tree of the proteins can be statistically inferred. The amino acid sites that have experienced convergent or parallel changes on independent evolutionary lineages can then be identified by comparing the amino acids at the beginning and end of each lineage. At present, the efficiency of the methods of ancestral sequence inference in identifying convergent and parallel changes is unknown. More seriously, when we identify convergent or parallel changes, it is unclear whether these changes are attributable to random chance. For these reasons, claims of convergent and parallel evolution at the amino acid sequence level have been disputed. We have conducted computer simulations to assess the efficiencies, of the parsimony and Bayesian methods of ancestral sequence inference in identifying convergent and parallel-change sites. Our results showed that the Bayesian method performs better than the parsimony method in identifying parallel changes, and both methods are inefficient in identifying convergent changes. However, the Bayesian method is recommended for estimating the number of convergent-change sites because it gives a conservative estimate. We have developed statistical tests for examining whether the observed numbers of convergent and parallel changes are due to random chance. As an example, we reanalyzed the stomach lysozyme sequences of foregut fermenters and found that parallel evolution is statistically significant, whereas convergent evolution is not well supported.

Amino Acid Sequence↗
PubMed2006

Inference in multiply sectioned Bayesian networks: methods and performance comparison.

This paper extends lazy propagation for inference in single-agent Bayesian networks (BNs) to multiagent lazy inference in multiply sectioned BNs (MSBNs). Two methods are proposed using distinct runtime structures. It was proved that the new methods are exact and efficient when the domain structure is sparse. Both improve space and time complexity more than the existing method, which allows multiagent probabilistic reasoning to be performed in much larger domains given the computational resource. The relative performances of the three methods are compared analytically and experimentally.

Algorithms↗
PubMed2005

BioVision: an application for the automated image analysis of histological sections.

We describe a computer application, "BioVision", that can be trained to quickly and effectively classify and quantify user definable histological objects (e.g., senile plaques, neurofibrillary tangles) within single or double-labeled immunocytochemically stained sections. For a given image population, BioVision is interactively trained (in Independent User Mode) by an investigator to perform the desired classifications. This training yields a statistical model of the different types of objects occurring in the target image population. The resulting model can then be used (in Automated User Mode) to classify all objects in any image or images from the target population. BioVision simplifies the quantification of complex visual objects and improves inter-rater reliability. The program accomplishes classification in two major stages: pixel classification and blob classification. In pixel classification, each pixel is assigned to one of some number of substance classes, based on its chromatic properties and local context, reflecting basic histological distinctions of interest. In the blob classification phase, the image's pixels are first partitioned into "blobs": maximal connected sets of pixels assigned to the same substance class. Then, based on its size, shape, textural and contextual properties, each blob is assigned to a histological object class. A Bayesian classifier is used in each of the pixel and blob classification stages. We report several tests of BioVision. First, we applied BioVision to classify senile plaques and neurofibrillary tangles in several test cases of Alzheimer's brain immunostained for beta-amyloid and PHF-tau and compared the results to those produced by experienced investigators. BioVision was trained to classify Plaque-type blobs as either plaques or plaque-type nonentities, and tangle-type blobs as either tangles or tangle-type nonentities. BioVision classified the objects with an accuracy comparable to the trained investigator. Next, we applied BioVision to the task of counting all the tangles in hippocampal images from 22 Alzheimer's disease (AD) cases selected to span a broad range of dementia levels from the tissue repository of UC Irvine's Center for the study of Brain Aging and Dementia. The tangle counts produced by BioVision proved to be significantly better predictors of the cases' adjusted MMSE scores than any of tangle load, age at death, post mortem interval or the interval between the last MMSE score and death.

Alzheimer Disease↗
PubMed2005

A fusion-based clinical decision support for disease diagnosis from endoscopic images.

This paper presents an intelligent decision support system designed on a decision fusion framework coupled with a priori knowledge base for abnormality detection from endoscopic images. Sub-decisions are made based on associated component feature sets derived from the endoscopic images and predefined algorithms, and subsequently fused to classify the patient state. Bayesian probability computations are employed to evaluate the accuracies of sub-decisions, which are utilized in estimating the probability of the fused decision. The overall detectability of abnormalities by using the proposed fusion approach is improved in terms of detection of true positive and true negative conditions when compared with corresponding results from individual methods.

Algorithms↗
PubMed2002

A Bayesian analysis of a proportion under non-ignorable non-response.

The National Health Interview Survey (NHIS) is one of the surveys used to assess one aspect of the health status of the U.S. population. One indicator of the nation's health is the total number of doctor visits made by the household members in the past year. We study the binary variable of at least one doctor visit versus no doctor visit by all household members to each of the 50 states and the District of Columbia. The proportion of households with at least one doctor visit is an indicator of the status of health of the U.S. population. There is a substantial number of non-respondents among the sampled households. The main issue we address here is that the non-response mechanism should not be ignored because respondents and non-respondents differ. The purpose of this work is to estimate the proportion of households with at least one doctor visit, and to investigate what adjustment needs to be made for non-ignorable non-response. We consider a non-ignorable non-response model that expresses uncertainty about ignorability through the ratio of odds of a household doctor visit among respondents to the odds of doctor visit among all households, and this ratio varies from state to state. We use a hierarchical Bayesian selection model to accommodate this non-response mechanism. Because of the weak identifiability of the parameters, it is necessary to 'borrow strength' across states as in small area estimation. We also perform a simulation study to compare the expansion model with an alternative expansion model, an ignorable model and a non-ignorable model. Inference for the probability of a doctor visit is generally similar across the models. Our main result is that for some of the states the non-response mechanism can be considered non-ignorable, and that 95 per cent credible intervals of the probability for a household doctor visit and the probability that a household responds shed important light on the NHIS data.

Bayes Theorem↗
PubMed2005

Propagation of population pharmacokinetic information using a Bayesian approach: comparison with meta-analysis.

We investigated the propagation of population pharmacokinetic information across clinical studies by applying Bayesian techniques. The aim was to summarize the population pharmacokinetic estimates of a study in appropriate statistical distributions in order to use them as Bayesian priors in consequent population pharmacokinetic analyses. Various data sets of simulated and real clinical data were fitted with WinBUGS, with and without informative priors. The posterior estimates of fittings with non-informative priors were used to build parametric informative priors and the whole procedure was carried on in a consecutive manner. The posterior distributions of the fittings with informative priors where compared to those of the meta-analysis fittings of the respective combinations of data sets. Good agreement was found, for the simulated and experimental datasets when the populations were exchangeable, with the posterior distribution from the fittings with the prior to be nearly identical to the ones estimated with meta-analysis. However, when populations were not exchangeble an alternative parametric form for the prior, the natural conjugate prior, had to be used in order to have consistent results. In conclusion, the results of a population pharmacokinetic analysis may be summarized in Bayesian prior distributions that can be used consecutively with other analyses. The procedure is an alternative to meta-analysis and gives comparable results. It has the advantage that it is faster than the meta-analysis, due to the large datasets used with the latter and can be performed when the data included in the prior are not actually available.

Administration, Oral↗
PubMedDate not supplied

Scatter rejection in modular gamma cameras for use in dynamic 3D spect brain imaging system.

All gamma cameras used in nuclear medicine have finite energy resolution, thereby making discrimination of scattered radiation from photopeak events extremely difficult. Different scatter-correction methods have been developed to prevent scattered radiation from degrading the image quality of a gamma-ray image. Rejecting scatter events on an event-by-event basis in data acquisition as much as possible is a preferable option. However, the fact that all scattered events cannot be rejected without sacrificing primary photon sensitivity might require applying scatter correction after acquisition; nevertheless, the scattered photons should not be allowed to enter into the image. Most conventional approaches reject scatter events by using energy windows (EWs). Milster et al. introduced the likelihood window (LW) as an alternative. We also employed this method in a modular gamma scintillation camera that has a 2 x 2 array of photomultipliers (PMTs) coupled to a NaI(TI) crystal. In this work, another scatter rejection scheme, the Bayesian window (BW), is developed. In contrast to the LW and the EW, the BW takes into account the simulated or actual scatter spectrum in making a decision to accept or reject a particular photon. This work compares the effectiveness of scatter rejection by BW, LW, and EW through receiver operating characteristic (ROC) studies. Results indicate that the BW method is the preferable choice.

Bayes Theorem↗
PubMed2002

Computerized lesion detection on breast ultrasound.

We investigated the use of a radial gradient index (RGI) filtering technique to automatically detect lesions on breast ultrasound. After initial RGI filtering, a sensitivity of 87% at 0.76 false-positive detections per image was obtained on a database of 400 patients (757 images). Next, lesion candidates were segmented from the background by maximizing an average radial gradient (ARD) index for regions grown from the detected points. At an overlap of 0.4 with a radiologist lesion outline, 75% of the lesions were correctly detected. Subsequently, round robin analysis was used to assess the quality of the classification of lesion candidates into actual lesions and false-positives by a Bayesian neural network. The round robin analysis yielded an Az value of 0.84, and an overall performance by case of 94% sensitivity at 0.48 false-positives per image. Use of computerized analysis of breast sonograms may ultimately facilitate the use of sonography in breast cancer screening programs.

Bayes Theorem↗
PubMed2005

Prediction of siRNA knockdown efficiency using artificial neural network models.

Selective knockdown of gene expression by short interference RNAs (siRNAs) has allowed rapid validation of gene functions and made possible a high throughput, genome scale approach to interrogate gene function. However, randomly designed siRNAs display different knockdown efficiencies of target genes. Hence, various prediction algorithms based on siRNA functionality have recently been constructed to increase the likelihood of selecting effective siRNAs, thereby reducing the experimental cost. Toward this end, we have trained three Back-propagation and Bayesian neural network models, previously not used in this context, to predict the knockdown efficiencies of 180 experimentally verified siRNAs on their corresponding target genes. Using our input coding based primarily on RNA structure thermodynamic parameters and cross-validation method, we showed that our neural network models outperformed most other methods and are comparable to the best predicting algorithm thus far published. Furthermore, our neural network models correctly classified 74% of all siRNAs into different efficiency categories; with a correlation coefficient of 0.43 and receiver operating characteristic curve score of 0.78, thus highlighting the potential utility of this method to complement other existing siRNA classification and prediction schemes.

Algorithms↗
PubMed1998

Can we learn to distinguish between "drug-like" and "nondrug-like" molecules?

We have used a Bayesian neural network to distinguish between drugs and nondrugs. For this purpose, the CMC acts as a surrogate for drug-like molecules while the ACD is a surrogate for nondrug-like molecules. This task is performed by using two different set of 1D and 2D parameters. The 1D parameters contain information about the entire molecule like the molecular weight and the the 2D parameters contain information about specific functional groups within the molecule. Our best results predict correctly on over 90% of the compounds in the CMC while classifying about 10% of the molecules in the ACD as drug-like. Excellent generalization ability is shown by the models in that roughly 80% of the molecules in the MDDR are classified as drug-like. We propose to use the models to design combinatorial libraries. In a computer experiment on generating a drug-like library of size 100 from a set of 10 000 molecules we obtain at least a 3 or 4 order of magnitude improvement over random methods. The neighborhoods defined by our models are not similar to the ones generated by standard Tanimoto similarity calculations. Therefore, new and different information is being generated by our models, and so it can supplement standard diversity approaches to library design.

Bayes Theorem↗
PubMed1985

[Alzheimer's presenile dementia transmitted in an extended kindred].

Forty-three patients affected with Alzheimer's disease were identified in a kindred of Italian origin, emigrated in part to the U.S.A. and France. Thirteen were known by history, 21 by medical record, and 9 by personal examination, of whom 5 were confirmed histopathologically. The clinical picture was fairly uniform: the first symptom was memory loss beginning around age 40. Psychotic-like symptoms often followed, with rapid evolution into profound dementia, and death around age 50. Akinesia was prominent at a late stage, often with myoclonus. Grand mal seizures sometimes occurred, with occasional interictal spike and wave discharge; repetitive paroxystic periodic discharges were never recorded. A genealogical study, as far as possible free from line bias, has been conducted mainly by analysis of municipal records. 1 435 subjects in 10 generations, linked to affected subjects through ascent/descent or marriage, were listed in a computer file; the corresponding genealogical tree or selected part thereof are generated by computer. Application of Bayesian techniques to demographic data makes possible an estimation of disease probability in subjects for which no clinical data were available: such an estimate was confirmed by the later discovery of a living patient in descent of a subject with 0.7 estimated disease probability. No patient was found in descent from an inbred union known as such. Patients are the only transmitters. The sex ratio is not significantly different from 1. There is no detectable maternal effect. The segregation ratio, as calculated from extensively known sibships, lies in the range 0.65 to 0.89; the lower value itself is significatively higher than the 0.5 value expected in an autosomal dominant monogenic Mendelian transmission. An environment factor is ruled out by the diversity of locations and circumstances in kindred members. Such a kindred may represent an useful model for fundamental studies in Alzheimer's disease and senile dementia of the Alzheimer type.

Adolescent↗
PubMed2005

A benchmark methodology for managing uncertainties in urban runoff quality models.

In this paper we present a benchmarking methodology, which aims at comparing urban runoff quality models, based on the Bayesian theory. After choosing the different configurations of models to be tested, this methodology uses the Metropolis algorithm, a general MCMC sampling method, to estimate the posterior distributions of the models' parameters. The analysis of these posterior distributions allows a quantitative assessment of the parameters' uncertainties and their interaction structure, and provides information about the sensitivity of the probability distribution of the model output to parameters. The effectiveness and efficiency of this methodology are illustrated in the context of 4 configurations of pollutants' accumulation/erosion models, tested on 4 street subcatchments. Calibration results demonstrate that the Metropolis algorithm produces reliable inferences of parameters thus, helping on the improvement of the mathematical concept of model equations.

Algorithms↗
PubMed2005

Single patient (n-of-1) trials with binary treatment preference.

The use of a fully parametric Bayesian method for analysing single patient trials based on the notion of treatment 'preference' is described. This Bayesian hierarchical modelling approach allows for full parameter uncertainty, use of prior information and the modelling of individual and patient sub-group structures. It provides updated probabilistic results for individual patients, and groups of patients with the same medical condition, as they are sequentially enrolled into individualized trials using the same medication alternatives. Two clinically interpretable criteria for determining a patient's response are detailed and illustrated using data from a previously published paper under two different prior information scenarios.

Amitriptyline↗
PubMed2004

A comparison of learning algorithms for Bayesian networks: a case study based on data from an emergency medical service.

Due to the uncertainty of many of the factors that influence the performance of an emergency medical service, we propose using Bayesian networks to model this kind of system. We use different algorithms for learning Bayesian networks in order to build several models, from the hospital manager's point of view, and apply them to the specific case of the emergency service of a Spanish hospital. This first study of a real problem includes preliminary data processing, the experiments carried out, the comparison of the algorithms from different perspectives, and some potential uses of Bayesian networks for management problems in the health service.

Algorithms↗
PubMed2004

Analysis of lesion detectability in Bayesian emission reconstruction with nonstationary object variability.

Bayesian methods based on the maximum a posteriori principle (also called penalized maximum-likelihood methods) have been developed to improve image quality in emission tomography. To explore the full potential of Bayesian reconstruction for lesion detection, we derive simplified theoretical expressions that allow fast evaluation of the detectability of a lesion in Bayesian reconstruction. This work is builded on the recent progress on the theoretical analysis of image properties of statistical reconstructions and the development of numerical observers. We explicitly model the nonstationary variation of the lesion and background without assuming that they are locally stationary. The results can be used to choose the optimum prior parameters for the maximum lesion detectability. The theoretical results are validated using Monte Carlo simulations. The comparisons show good agreement between the theoretical predictions and the Monte Carlo results. We also demonstrate that the lesion detectability can be reliably estimated using one noisy data set.

Algorithms↗
PubMed2006

Model-free deconvolution of femtosecond kinetic data.

Though shorter laser pulses can also be produced, pulses of the 100 fs range are typically used in femtosecond kinetic measurements, which are comparable to characteristic times of the studied processes, making detection of the kinetic response functions inevitably distorted by convolution with the pulses applied. A description of this convolution in terms of experiments and measurable signals is given, followed by a detailed discussion of a large number of available methods to solve the convolution equation to get the undistorted kinetic signal, without any presupposed kinetic or photophysical model of the underlying processes. A thorough numerical test of several deconvolution methods is described, and two iterative time-domain methods (Bayesian and Jansson deconvolution) along with two inverse filtering frequency-domain methods (adaptive Wiener filtering and regularization) are suggested to use for the deconvolution of experimental femtosecond kinetic data sets. Adaptation of these methods to typical kinetic curve shapes is described in detail. We find that the model-free deconvolution gives satisfactory results compared to the classical "reconvolution" method where the knowledge of the kinetic and photophysical mechanism is necessary to perform the deconvolution. In addition, a model-free deconvolution followed by a statistical inference of the parameters of a model function gives less biased results for the relevant parameters of the model than simple reconvolution. We have also analyzed real-life experimental data and found that the model-free deconvolution methods can be successfully used to get undistorted kinetic curves in that case as well. A graphical computer program to perform deconvolution via inverse filtering and additional noise filters is also provided as Supporting Information. Though deconvolution methods described here were optimized for femtosecond kinetic measurements, they can be used for any kind of convolved data where measured experimental shapes are similar.

Journal Article↗
PubMed2004

Bayesian estimation of survival functions under stochastic precedence.

When estimating the distributions of two random variables, X and Y, investigators often have prior information that Y tends to be bigger than X. To formalize this prior belief, one could potentially assume stochastic ordering between X and Y, which implies Pr(X < or = z) > or = Pr(Y < or = z) for all z in the domain of X and Y. Stochastic ordering is quite restrictive, though, and this article focuses instead on Bayesian estimation of the distribution functions of X and Y under the weaker stochastic precedence constraint, Pr(X < or = Y) > or = 0.5. We consider the case where both X and Y are categorical variables with common support and develop a Gibbs sampling algorithm for posterior computation. The method is then generalized to the case where X and Y are survival times. The proposed approach is illustrated using data on survival after tumor removal for patients with malignant melanoma.

Bayes Theorem↗