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PubMed1997

Dichroic statistical model for prediction and analysis of peptide helicity.

Traditional statistical models for the prediction of peptide helicity are written in terms of the mean fractional helicity of the peptide residues. Far ultraviolet circular dichroic measurements of peptide solutions are converted to mean fractional helicity by partitioning the observed ellipticity between that of a perfect helix and a random coil. This partition does not adequately represent the ensemble of peptide molecules present in solution that populate imperfect helical conformations of quite variable lengths. A new dichroic statistical model has been written in terms of ellipticity rather than fractional helical content that recognizes (1) the source of ellipticity, peptide bond adsorption; (2) the differential ellipticity of peptide bonds in the terminal and interior helical turns; and (3) the contributions of each participant in a conformational ensemble to the observed ellipticity. Comparative analyses of host/guest peptides indicates that significant differences are obtained between residue w and n weights and ellipticity values using the traditional and dichroic statistical models.

Algorithms↗
PubMed2004

Primer design and marker clustering for multiplex SNP-IT primer extension genotyping assay using statistical modeling.

MOTIVATION: The optimization of the primer design is critical for the development of high-throughput SNP genotyping methods. Recently developed statistical models of the SNP-IT primer extension genotyping reaction allow further improvement of primer quality for the assay. RESULTS: Here we describe how the statistical models can be used to improve primer design for the assay. We also show how to optimize clustering of the SNP markers into multiplex panels using statistical model for multiplex SNP-IT. The primer set failure probability calculated by a model is used as a minimization function for both primer selection and primers clustering. Three clustering algorithms for the multiplex genotyping SNP-IT assay are described and their relative performance is evaluated. We also describe the approaches to improve the speed of primer design and clustering calculations when using the statistical models. Our clustering decreases the average failure probability of the marker set by 7-25%. The experimental marker failure rate in the multiplex reaction was reduced dramatically and success rate can be achieved as high as 96%. AVAILABILITY: The primer design using statistical models is freely available from www.autoprimer.com.

Algorithms↗
PubMed1984

Origin and characterization of departures from the statistical model of component-peak overlap in chromatography.

A statistical model of component-peak overlap in complex chromatograms is reviewed. Procedures for the estimation of the number of components in an analyte from its chromatograms by means of this model are restated. We note that the statistical model does not account for the effects of certain realistic chromatographic attributes. The influences of component-peak density, amplitude range, asymmetry, and noise levels on the estimation of the average component number are determined empirically with computer-generated chromatograms and are quantified by analyses of variance. We find that small departures from the model arise from variations in the magnitude of the amplitude range, density and the noise level. A large departure from theory arises from an application of the model to chromatograms containing highly asymmetric component-peaks. In spite of these departures, the estimation of the component number from chromatograms containing randomly distributed Gaussian component-peaks is uniformly more accurate with the use of the model than from a counting of peak maximum in chromatograms of extraordinarily high resolving power.

Analysis of Variance↗
PubMed2004

Predicting extubation outcome in preterm newborns: a comparison of neural networks with clinical expertise and statistical modeling.

Even though ventilator technology and monitoring of premature infants has improved immensely over the past decades, there are still no standards for weaning and determining optimal extubation time for those infants. Approximately 30% of intubated preterm infants will fail attempted extubation, requiring reintubation and resuming of mechanical ventilation. A machine-learning approach using artificial neural networks (ANNs) to aid in extubation decision making is hereby proposed. Using expert opinion, 51 variables were identified as being relevant for the decision of whether to extubate an infant who is on mechanical ventilation. The data on 183 premature infants, born between 1999 and 2002, were collected by review of medical charts. The ANN extubation model was compared with alternative statistical modeling using multivariate logistic regression and also with the clinician's own predictive insight using sensitivity analysis and receiver operating characteristic curves. The optimal ANN model used 13 parameters and achieved an area under the receiver operating characteristic curve of 0.87 (out-of-sample validation), comparing favorably with multivariate logistic regression. It also compared well with the clinician's expertise, which raises the possibility of being useful as an automated alert tool. Because an ANN learns directly from previous data obtained in the institution where it is to be used, this makes it particularly amenable for application to evidence-based medicine. Given the variety of practices and equipment being used in different hospitals, this may be particularly relevant in the context of caring for preterm newborns who are on mechanical ventilation.

Decision Making↗
PubMed1991

Statistical models of the overdispersed molecular clock.

The most commonly used statistical model to describe the rate constancy of molecular evolution (molecular clock) is a simple Poisson process in which the variance of the number of amino acid or nucleotide substitutions in a particular gene should be equal to the mean and henceforth the dispersion index, the ratio of the variance to the mean, should be equal to one. Recent sequence data, however, have shown that the substitutional process in molecular evolution is often considerably overdispersed and have called into question the generality of using a simple Poisson process. Several efforts have been made to develop more realistic models of molecular evolution. In this paper, I will show that the spatial (site-specific) variation in the rate of molecular evolution is an improbable cause of the overdispersion and then review various statistical models which take the temporal variation into account. Although these models do not immediately specify what the mechanisms of molecular evolution might be, they do make qualitatively different predictions and give some insight into their inference. One way to distinguish them is suggested. In addition, effects of selected substitutions that presumably occur after a major change in a molecule are quasi-quantitatively examined. It is most likely that the overdispersion of molecular clock is due either to a major molecular reconfiguration (fluctuating neutral space) led by a series of subliminal neutral changes or to selected substitutions fine-tuning a molecule after a major molecular change. Although the latter possibility, of course, violates the simplest neutrality assumption, it would not impair the neutral theory as a whole.

Biological Evolution↗
PubMed2003

Statistical model for diffusion attenuated MR signal.

A general statistical model that can describe a rather large number of experimental results related to the structure of the diffusion-attenuated MR signal in biological systems is introduced. The theoretical framework relies on a phenomenological model that introduces a distribution function for tissue apparent diffusion coefficients (ADC). It is shown that at least two parameters--the position of distribution maxima (ADC) and the distribution width (sigma)--are needed to describe the MR signal in most regions of a human brain. A substantial distribution width, on the order of 36% of the ADC, was found for practically all brain regions examined. This method of modeling the MR diffusion measurement allows determination of an intrinsic tissue-specific ADC for a given diffusion time independent of the strength of diffusion sensitizing gradients. The model accounts for the previously found biexponential behavior of the diffusion-attenuated MR signal in CNS.

Brain↗
PubMed1996

The utility of optical waveguide DNA array hybridization and melting for rapid resolution of mismatches, and for detection of minor mutant components in the presence of a majority of wild type sequence: Statistical model and supporting data.

We have derived a statistical model to determine binding and dissociation kinetics of oligonucleotide-antibody-microparticle complexes to oligonucleotides on a surface illuminated by the evanescent wave. The model supports the extremely rapid binding kinetics previously observed and predicts very rapid thermal dissociation kinetics. Experimental results confirmed the model and showed that the most rapid heating available achieved meltoff in 8 s with discrimination of single base differences. Similarly, a model of particulate labels with multiple points of interaction predicted that the system would be very sensitive to a minority of matched interactions in the presence of a majority of mismatched interactions. Conversely, the system would be relatively insensitive to the presence of a higher proportion of matched species. These predictions are born out by experiment. A 1:10 mixture of matched:mismatched gave a melting curve which was approximately the sum of melting curves of the two species alone, and a 50:50 mixture gave a melting curve almost indistinguishable from the highest melting component. Thus, the system will be amenable to detection of infrequent mutations in the presence of an excess of the wild type allele provided the mutant sequence is present on the chip. Further, it lends itself to high throughput automation due to the rapidity with which the thermal melt can be achieved.

DNA↗
PubMed1976

An interpretation of the drop ball test in terms of a statistical model for fracture.

Some well known statistical models for fracture were applied to the problem of impact of glass ophthalmic lenses. Parameters for a flaw distribution function were chosen to fit some previously published results of drop ball testing. The concept of an impact-resistant lens was reformulated in terms of minimizing the most severe flaws. Formulas were derived for the detection parameter, a convenient measure of the ability of any mechanical test to identify those lenses with the worst flaws. It was shown that the fraction of such lenses identified by the drop ball test is very small. As a side note, it was found that it is not significantly more difficult to expose "bad" lenses when they are tested in plastic bags. A method of obtaining useful information from the results of drop ball testing was outlined.

Eyeglasses↗
PubMed2004

A statistical model for contours in images.

In this paper, we describe a statistical model for the gradient vector field of the gray level in images validated by different experiments. Moreover, we present a global constrained Markov model for contours in images that uses this statistical model for the likelihood. Our model is amenable to an Iterative Conditional Estimation (ICE) procedure for the estimation of the parameters; our model also allows segmentation by means of the Simulated Annealing (SA) algorithm, the Iterated Conditional Modes (ICM) algorithm, or the Modes of Posterior Marginals (MPM) Monte Carlo (MC) algorithm. This yields an original unsupervised statistical method for edge-detection, with three variants. The estimation and the segmentation procedures have been tested on a total of 160 images. Those tests indicate that the model and its estimation are valid for applications that require an energy term based on the log-likelihood ratio. Besides edge-detection, our model can be used for semiautomatic extraction of contours, localization of shapes, non-photo-realistic rendering; more generally, it might be useful in various problems that require a statistical likelihood for contours.

Algorithms↗
PubMed1996

Use of statistical models to evaluate racing performance in thoroughbreds.

OBJECTIVE: To develop a statistical model to evaluate the influence of specific parameters on racing performance in Thoroughbreds. DESIGN: Survey. SAMPLE POPULATION: Racing records of Thoroughbreds performing in Louisiana from 1981 to 1985. PROCEDURE: Race results for 20 randomly selected days from 5 racetracks during 5 years were analyzed, using regression analysis. RESULTS: The most influential parameter was distance raced. There were significant differences in racing performance among horses at different tracks. At the tracks examined, fast racing surfaces resulted in significantly faster finish times than good racing surfaces, and good racing surfaces resulted in significantly faster finish times than muddy racing surfaces. Finish times also were significantly faster as a function of increasing purse amount and age of horse. Finish times were significantly faster during quarter 4 (summer) and significantly slower during quarter 1 (fall). Races were run at significantly faster times as the number of the race progressed during the day. The finish times were significantly slower as the number of horses competing in the race increased, as the weight carried by the horse increased, and as the starting position away from the rail increased. CLINICAL IMPLICATIONS: The coefficients used in the statistical model of this report may be used by researchers to compare before injury and after treatment finish times. The coefficients can be used to standardize past racing performances to uniform conditions, thus permitting comparison from 1 race to another to determine success of treatments and to inform owners of prognosis.

Animals↗
PubMed2001

Statistical models to predict the need for postoperative intensive care and hospitalization in pediatric surgical patients.

OBJECTIVE: To develop statistical models for predicting postoperative hospital and ICU stay in pediatric surgical patients based on preoperative clinical characteristics and operative factors related to the degree of surgical stress. We hypothesized that preoperative and operative factors will predict the need for ICU admission and may be used to forecast the length of ICU stay or postoperative hospital stay. DESIGN: Prospective data collection from 1,763 patients. SETTING: Tertiary care children's hospital. PATIENTS AND PARTICIPANTS: All pediatric surgical patients, including those undergoing day surgery. Patients undergoing dental or ophthalmologic surgical procedures were excluded. INTERVENTIONS: None. MEASUREMENTS AND RESULTS: A logistic regression model predicting ICU admission was developed from all patients. Poissonregression models were developed from 1,161 randomly selected patients and validated from the remaining 602 patients. The logistic regression model for ICU admission was highlypredictive (area under the receiver operating characteristics (ROC) curve = 0.981). In the data set used for development of Poisson regression models, significant correlations occurred between the observed and predicted ICU stay (Pearson r = 0.468, p < 0.0001, n = 131) and between the observed and predicted hospital stay for patients undergoing general (r = 0.695, p < 0.0001), orthopedic (r = 0.717, p < 0.0001), cardiothoracic (r = 0.746, p < 0.0001), urologic (r = 0.458, p < 0.0001), otorhinolaryngologic (r = 0.962, p < 0.0001), neurosurgical (r = 0.7084, p < 0.0001) and plastic surgical (r = 0.854, p < 0.0001) procedures. In the validation data set, correlations between predicted and observed hospital stay were significant for general (p < 0.0001), orthopedic (p < 0.0001), cardiothoracic (p = 0.0321) and urologic surgery (p = 0.0383). The Poisson models for length of ICU stay, otorhinolaryngology, neurosurgery or plastic surgery could not be validated because of small numbers of patients. CONCLUSIONS: Preoperative and operative factors may be used to develop statistical models predicting the need for ICU admission in pediatric surgical patients, and hospital stay following general surgical, orthopedic, cardiothoracic and urologic procedures. These statistical models need to be refined and validatedfurther, perhaps using data collection from multiple institutions.

Arkansas↗
PubMed2006

Hidden Markov models based on symbolic dynamics for statistical modeling of cardiovascular control in hypertensive pregnancy disorders.

Discrete hidden Markov models (HMMs) were applied to classify pregnancy disorders. The observation sequence was generated by transforming RR and systolic blood pressure time series using symbolic dynamics. Time series were recorded from 15 women with pregnancy-induced hypertension, 34 with preeclampsia and 41 controls beyond 30th gestational week. HMMs with five to ten hidden states were found to be sufficient to characterize different blood pressure variability, whereas significant classification in RR-based HMMs was found using fifteen hidden states. Pregnancy disorders preeclampsia and pregnancy induced hypertension revealed different patho-physiological autonomous regulation supposing different etiology of both disorders.

Algorithms↗
PubMed2000

Predicting particulate (PM10) personal exposure distributions using a random component superposition statistical model.

This paper presents a new statistical model designed to extend our understanding from prior personal exposure field measurements of urban populations to other cities where ambient monitoring data, but no personal exposure measurements, exist. The model partitions personal exposure into two distinct components: ambient concentration and nonambient concentration. It is assumed the ambient and nonambient concentration components are uncorrelated and add together; therefore, the model is called a random component superposition (RCS) model. The 24-hr ambient outdoor concentration is multiplied by a dimensionless "attenuation factor" between 0 and 1 to account for deposition of particles as the ambient air infiltrates indoors. The RCS model is applied to field PM10 measurement data from three large-scale personal exposure field studies: THEES (Total Human Environmental Exposure Study) in Phillipsburg, NJ; PTEAM (Particle Total Exposure Assessment Methodology) in Riverside, CA; and the Ethyl Corporation study in Toronto, Canada. Because indoor sources and activities (smoking, cooking, cleaning, the personal cloud, etc.) may be similar in similar populations, it was hypothesized that the statistical distribution of nonambient personal exposure is invariant across cities. Using a fixed 24-hr attenuation factor as a first approximation derived from regression analysis for the respondents, the distributions of nonambient PM10 personal exposures were obtained for each city. Although the mean ambient PM10 concentrations in the three cities varied from 27.9 micrograms/m3 in Toronto to 60.9 micrograms/m3 in Phillipsburg to 94.1 micrograms/m3 in Riverside, the mean nonambient components of personal exposures were found to be closer: 52.6 micrograms/m3 in Toronto; 52.4 micrograms/m3 in Phillipsburg; and 59.2 micrograms/m3 in Riverside. The three frequency distributions of the nonambient components of exposure also were similar in shape, giving support to the hypothesis that nonambient concentrations are similar across different cities and populations. These results indicate that, if the ambient concentrations were completely controlled and set to zero in all three cities, the median of the remaining personal exposures to PM10 would range from 32.0 micrograms/m3 (Toronto) to 34.4 micrograms/m3 (Phillipsburg) to 48.8 micrograms/m3 (Riverside). The highest-exposed 30% of the population in the three cities would still be exposed to 24-hr average PM10 concentrations of 47-74 micrograms/m3; the highest 20% would be exposed to concentrations of 56-92 micrograms/m3; the highest 10% to concentrations of 88-131 micrograms/m3; and the highest 5% to 133-175 micrograms/m3, due only to indoor sources and activities. The distribution for the difference between personal exposures and indoor concentrations, or the "personal cloud," also was similar in the three cities, with a mean of 30-35 micrograms/m3, suggesting that the personal cloud accounts for more than half of the nonambient component of PM10 personal exposure in the three cities. Using only the ambient measurements in Toronto, the nonambient data from THEES in Phillipsburg was used to predict the entire personal exposure distribution in Toronto. The PM10 exposure distribution predicted by the model showed reasonable agreement with the PM10 personal exposure distribution measured in Toronto. These initial results suggest that the RCS model may be a powerful tool for predicting personal exposure distributions and statistics in other cities where only ambient particle data are available.

Air Pollution↗
PubMed2005

QSPR modeling of pseudoternary microemulsions formulated employing lecithin surfactants: application of data mining, molecular and statistical modeling.

Data mining, computer aided molecular modeling, descriptor calculation, genetic algorithm and multiple linear regression analysis techniques were combined together to generate predictive quantitative structure property relationship (QSPR) models explaining the formation of lecithin-based W/O microemulsions. Ninety-four microemulsion phase diagrams were collected from five different references published over the past few years. Computer-based molecular modeling techniques were then applied on the components of the collected microemulsion systems to generate corresponding plausible three-dimensional (3D) structures. The resulting 3D models were utilized to calculate a group of molecular physicochemical descriptors. Thereafter, genetic algorithm and backward stepwise regression analysis were separately assessed as means for selecting optimal descriptor sets for statistical modeling. The selected descriptors were correlated with microemulsion existence areas employing multiple linear regression analysis. The resulting W/O models were statistically validated and found to be of significant predictive power. The models allowed better understanding of the process of microemulsion formation. Unfortunately, all QSPR modeling efforts directed towards O/W microemulsions failed completely.

Emulsions↗
PubMed2003

A statistical model for identifying proteins by tandem mass spectrometry.

A statistical model is presented for computing probabilities that proteins are present in a sample on the basis of peptides assigned to tandem mass (MS/MS) spectra acquired from a proteolytic digest of the sample. Peptides that correspond to more than a single protein in the sequence database are apportioned among all corresponding proteins, and a minimal protein list sufficient to account for the observed peptide assignments is derived using the expectation-maximization algorithm. Using peptide assignments to spectra generated from a sample of 18 purified proteins, as well as complex H. influenzae and Halobacterium samples, the model is shown to produce probabilities that are accurate and have high power to discriminate correct from incorrect protein identifications. This method allows filtering of large-scale proteomics data sets with predictable sensitivity and false positive identification error rates. Fast, consistent, and transparent, it provides a standard for publishing large-scale protein identification data sets in the literature and for comparing the results obtained from different experiments.

Amino Acid Sequence↗
PubMed1992

A statistical model based fundamental frequency synthesizer for Mandarin speech.

A novel method based on a statistical model for the fundamental-frequency (F0) synthesis in Mandarin text-to-speech is proposed. Specifically, a statistical model is employed to determine the relationship between F0 contour patterns of syllables and linguistic features representing the context. Parameters of the model were empirically estimated from a large training set of sentential utterances. Phonologic rules are then automatically deduced through the training process and implicitly memorized in the model. In the synthesis process, contextual features are extracted from a given input text, and the best estimates of F0 contour patterns of syllable are then found by a Viterbi algorithm using the well-trained model. This method can be regarded as employing a stochastic grammar to reduce the number of candidates of F0 contour pattern at each decision point of synthesis. Although linguistic features on various levels of input text can be incorporated into the model, only some relevant contextual features extracted from neighboring syllables were used in this study. Performance of this method was examined by simulation using a database composed of nine repetitions of 112 declarative sentential utterances of the same text, all spoken by a single speaker. By closely examining the well-trained model, some evidence was found to show that the declination effect as well as several sandhi rules are implicitly contained in the model. Experimental results show that 77.56% of synthesized F0 contours coincide with the VQ-quantized counterpart of the original natural speech. Naturalness of the synthesized speech was confirmed by an informal listening test.

China↗
PubMed1999

Subjective versus statistical model assessment of mortality risk in open heart surgical procedures.

BACKGROUND: The aim of this study was to compare the predictive accuracy for open heart surgical mortality between a statistical model based on collection of clinical data and surgeons' subjective risk assessment. METHODS: Predictive discrimination of both risk assessments (surgeons' and model) was compared through the area under the receiver operating characteristic curve. Logistic regression analysis was used to assess the relation between surgeons' and model predictions to actual outcomes. Calibration of the subjective estimates was evaluated with a chi2 test. RESULTS: Overall, the area under the receiver operating characteristic curve was 0.76 for the statistical model and 0.70 for the subjective assessment. Logistic regression analysis showed that the statistical model remained significant after accounting for the subjective assessment. Calibration of subjective mortality predictions was poor. CONCLUSIONS: Surgeons' risk assessment tends to cluster in the middle ranges of risk. Subjective assessment seems accurate in identifying the two extremes of risk but is inaccurate for intermediate risk levels. A multivariate statistical model improves the accuracy of subjective predictions.

Aged↗