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At least 487 records · Page 27Linked to original sources
PubMed2001

Assessment of response bias in mild head injury: beyond malingering tests.

The evaluation of response bias and malingering in the cases of mild head injury should not rely on a single test. Initial injury severity, typical neuropsychological test performance patterns, preexisting emotional stress or chronic social difficulties, history of previous neurological or psychiatric disorder, other system injuries sustained in the accident, preinjury alcohol abuse, and a propensity to attribute benign cognitive and somatic symptoms to a brain injury must be considered along with performances on specific measures of response bias. This article reviews empirically-supported tests and indices. Use of the likelihood ratio and other statistical indicators of diagnostic efficiency are demonstrated. Bayesian model averaging as a statistical technique to derive optimal prediction models is performed with a clinical data set.

Algorithms↗
PubMed2003

Geographical variation in the incidence of acute myocardial infarction in eastern Finland--a Bayesian perspective.

BACKGROUND: Large geographical variation in the incidence and mortality of cardiovascular disease (CHD) has been repeatedly reported in Finland with persistent difference between east and west. We undertook this study to estimate the geographical distribution of Acute Myocardial Infarction (AMI) incidence in the high-risk province of North Karelia and in the province of Kuopio. METHODS: Data on men aged 25-64 years with first event of acute myocardial infarction (AMI) were obtained from the FINMONICA AMI register, which recorded detailed information of AMI events during the period 1983 to 1992. The geographical pattern of AMI incidence was studied in two five-year periods 1983 to 1987 and 1988 to 1992 separately in 10 km x 10 km grid cells employing the Geographical Information System (GIS) and a Bayesian hierarchical approach. RESULTS: In both periods Bayesian modeling revealed a geographical pattern of AMI incidence and high risk (probability that incidence exceeds the observed mean incidence) in the remote rural areas. CONCLUSIONS: Detection of high-risk areas in both provinces showed that underlying environmental and/or genetic risk factors of AMI are not evenly distributed within the province but enriched in certain geographical non-administratively defined locations in eastern Finland.

Adult↗
PubMed2006

Phasic norepinephrine: a neural interrupt signal for unexpected events.

Extensive animal studies indicate that the neuromodulator norepinephrine plays an important role in specific aspects of vigilance, attention and learning, putatively serving as a neural interrupt or reset function. The activity of norepinephrine-releasing neurons in the locus coeruleus during attentional tasks is modulated not only by the animal's level of engagement and the sensory inputs, but also by temporally rich aspects of internal decision-making processes. Here, we propose that it is unexpected changes in the world within the context of a task that activate the noradrenergic interrupt signal. We quantify this idea in a Bayesian model of a well-studied visual discrimination task, demonstrating that the model captures a rich repertoire of noradrenergic responses at the sub-second temporal resolution.

Animals↗
PubMed2026

Mutational signatures in blood-brain barrier: mechanisms, computational insights, and clinical applications in precision oncology.

The blood - brain barrier (BBB) plays a central role in maintaining central nervous system (CNS) homeostasis, and its disruption is a defining feature of malignant brain tumors such as glioblastoma. Emerging evidence indicates that BBB dysfunction not only alters the tumor microenvironment but also shapes the mutational processes that drive genomic instability in CNS malignancies. This review synthesizes current understanding of the biological mechanisms linking BBB breakdown with distinct mutational signatures, including those arising from oxidative stress, hypoxia-induced replication stress, lipid peroxidation, inflammation, and metabolic reprogramming. Advances in next-generation sequencing, coupled with computational tools such as non-negative matrix factorization, Bayesian modeling, and deep learning, have enabled precise extraction of these signatures and their integration with multi-omics data. Clinically, BBB-associated mutational signatures offer significant promise for therapeutic stratification, prediction of treatment response, and noninvasive monitoring through cerebrospinal fluid - derived circulating tumor DNA. Despite these advances, challenges persist due to limited tissue accessibility, low-yield CSF samples, incomplete mechanistic models, and the lack of CNS-specific analytical frameworks. A deeper understanding of BBB-driven mutational processes, supported by improved computational approaches and integrative datasets, holds potential to advance precision oncology in neuro-oncology.

Humans↗
PubMed2003

A software package for cDNA microarray data normalization and assessing confidence intervals.

DNA microarray data are affected by variations from a number of sources. Before these data can be used to infer biological information, the extent of these variations must be assessed. Here we describe an open source software package, lcDNA, that provides tools for filtering, normalizing, and assessing the statistical significance of cDNA microarray data. The program employs a hierarchical Bayesian model and Markov Chain Monte Carlo simulation to estimate gene-specific confidence intervals for each gene in a cDNA microarray data set. This program is designed to perform these primary analytical operations on data from two-channel spotted, or in situ synthesized, DNA microarrays.

Bayes Theorem↗
PubMedDate not supplied

Merging microsatellite data.

Genotype calling procedures vary from laboratory to laboratory for many microsatellite markers. Even within the same laboratory, application of different experimental protocols often leads to ambiguities. The impact of these ambiguities ranges from irksome to devastating. Resolving the ambiguities can increase effective sample size and preserve evidence in favor of disease-marker associations. Because different data sets may contain different numbers of alleles, merging is unfortunately not a simple process of matching alleles one to one. Merging data sets manually is difficult, time-consuming, and error-prone due to differences in genotyping hardware, binning methods, molecular weight standards, and curve fitting algorithms. Merging is particularly difficult if few or no samples occur in common, or if samples are drawn from ethnic groups with widely varying allele frequencies. It is dangerous to align alleles simply by adding a constant number of base pairs to the alleles of one of the data sets. To address these issues, we have developed a Bayesian model and a Markov chain Monte Carlo (MCMC) algorithm for sampling the posterior distribution under the model. Our computer program, MicroMerge, implements the algorithm and almost always accurately and efficiently finds the most likely correct alignment. Common allele frequencies across laboratories in the same ethnic group are the single most important cue in the model. MicroMerge computes the allelic alignments with the greatest posterior probabilities under several merging options. It also reports when data sets cannot be confidently merged. These features are emphasized in our analysis of simulated and real data.

Algorithms↗
PubMed2004

Thirty-year trends in cardiovascular risk factor levels among US adults with diabetes: National Health and Nutrition Examination Surveys, 1971-2000.

Among US adults with diabetes, using data from the National Health and Nutrition Examination Survey for 1971-1974, 1976-1980, 1988-1994, and 1999-2000, the authors describe 30-year trends in total cholesterol, blood pressure, and smoking levels. Using Bayesian models, the authors calculated mean changes per year and 95% credible intervals for age-adjusted mean total cholesterol and blood pressure levels and the prevalence of high total cholesterol (> or =5.17 mmol/liter), high blood pressure (systolic blood pressure: > or =140 mmHg and/or diastolic blood pressure: > or =90 mmHg), and smoking. Between 1971-1974 and 1999-2000, mean total cholesterol declined from 5.95 mmol/liter to 5.48 mmol/liter (-0.02 (95% credible interval: -0.03, -0.01) mmol/liter per year). The proportion with high cholesterol decreased from 72% to 55%. Mean blood pressure declined from 146/86 mmHg to 134/72 mmHg (systolic blood pressure: -0.5 (95% credible interval: -1.1, 0.5) mmHg per year; diastolic blood pressure: -0.6 (95% credible interval: -1.0, -0.03) mmHg per year). The proportion with high blood pressure decreased from 64% to 37%, and smoking prevalence decreased from 32% to 17%. Although these trends are encouraging, still one of two people with diabetes has high cholesterol, one of three has high blood pressure, and one of six is a smoker.

Adult↗
PubMed2026

Identifying multigenic modules under selection in the tumor genome.

MOTIVATION: Genomic alterations in cancer arise from selective pressures acting on hallmark molecular modules, layered over a background of random mutagenic events. Methods to detect selection at the level of modules, as opposed to genes or nucleotides, are relatively underdeveloped. RESULTS: Here we present CanSRMaPP (Cancer Selection Recovery by Maximum Posterior Probability), a Bayesian model of the cancer genome that infers mutational selection on single genes and multi-genic modules while simultaneously modeling background events. Applying CanSRMaPP to lung adenocarcinoma genomes, we identify positive selection on 63 modules, yielding a model that parsimoniously explains the observed pattern of genetic alterations observed in new cancer cohorts. We further show that CanSRMaPP is adaptable to more tumor types and to alternative module definitions. We show that these modules serve as an effective scaffold for translating the cancer genome to molecular states, with prediction of cancer biomarker status as demonstration. AVAILABILITY: CanSRMaPP is freely available on GitHub. SUPPLEMENTARY INFORMATION: Supplementary Figs. S1-5, Supplementary Tables S1-5, and Supplementary Notes 1 and 2 are available at Bioinformatics online.

Journal Article↗
PubMed2003

Using credibility intervals instead of hypothesis tests in SAGE analysis.

MOTIVATION: Statistical methods usually used to perform Serial Analysis of Gene Expression (SAGE) analysis are based on hypothesis testing. They answer the biologist's question: 'what are the genes with differential expression greater than r with P-value smaller than P?'. Another useful and not yet explored question is: 'what is the uncertainty in differential expression ratio of a gene?'. RESULTS: We have used Bayesian model for SAGE differential gene expression ratios as a more informative alternative to hypothesis tests since it provides credibility intervals. AVAILABILITY: The model is implemented in R statistical language script and is available under GNU/GLP copyleft at supplemental web site. SUPPLEMENTARY INFORMATION: http://www.ime.usp.br/~rvencio/SAGEci/

Algorithms↗
PubMed2004

BioOptimizer: a Bayesian scoring function approach to motif discovery.

MOTIVATION: Transcription factors (TFs) bind directly to short segments on the genome, often within hundreds to thousands of base pairs upstream of gene transcription start sites, to regulate gene expression. The experimental determination of TFs binding sites is expensive and time-consuming. Many motif-finding programs have been developed, but no program is clearly superior in all situations. Practitioners often find it difficult to judge which of the motifs predicted by these algorithms are more likely to be biologically relevant. RESULTS: We derive a comprehensive scoring function based on a full Bayesian model that can handle unknown site abundance, unknown motif width and two-block motifs with variable-length gaps. An algorithm called BioOptimizer is proposed to optimize this scoring function so as to reduce noise in the motif signal found by any motif-finding program. The accuracy of BioOptimizer, which can be used in conjunction with several existing programs, is shown to be superior to using any of these motif-finding programs alone when evaluated by both simulation studies and application to sets of co-regulated genes in bacteria. In addition, this scoring function formulation enables us to compare objectively different predicted motifs and select the optimal ones, effectively combining the strengths of existing programs. AVAILABILITY: BioOptimizer is available for download at www.fas.harvard.edu/~junliu/BioOptimizer/

Algorithms↗
PubMed2006

Integrating multi-attribute similarity networks for robust representation of the protein space.

MOTIVATION: A global view of the protein space is essential for functional and evolutionary analysis of proteins. In order to achieve this, a similarity network can be built using pairwise relationships among proteins. However, existing similarity networks employ a single similarity measure and therefore their utility depends highly on the quality of the selected measure. A more robust representation of the protein space can be realized if multiple sources of information are used. RESULTS: We propose a novel approach for analyzing multi-attribute similarity networks by combining random walks on graphs with Bayesian theory. A multi-attribute network is created by combining sequence and structure based similarity measures. For each attribute of the similarity network, one can compute a measure of affinity from a given protein to every other protein in the network using random walks. This process makes use of the implicit clustering information of the similarity network, and we show that it is superior to naive, local ranking methods. We then combine the computed affinities using a Bayesian framework. In particular, when we train a Bayesian model for automated classification of a novel protein, we achieve high classification accuracy and outperform single attribute networks. In addition, we demonstrate the effectiveness of our technique by comparison with a competing kernel-based information integration approach.

Algorithms↗
PubMed2003

Incorporating prior beliefs about selection bias into the analysis of randomized trials with missing outcomes.

In randomized studies with missing outcomes, non-identifiable assumptions are required to hold for valid data analysis. As a result, statisticians have been advocating the use of sensitivity analysis to evaluate the effect of varying assumptions on study conclusions. While this approach may be useful in assessing the sensitivity of treatment comparisons to missing data assumptions, it may be dissatisfying to some researchers/decision makers because a single summary is not provided. In this paper, we present a fully Bayesian methodology that allows the investigator to draw a 'single' conclusion by formally incorporating prior beliefs about non-identifiable, yet interpretable, selection bias parameters. Our Bayesian model provides robustness to prior specification of the distributional form of the continuous outcomes.

Acquired Immunodeficiency Syndrome↗
PubMed1998

Bayesian mapping of multiple quantitative trait loci from incomplete inbred line cross data.

A novel fine structure mapping method for quantitative traits is presented. It is based on Bayesian modeling and inference, treating the number of quantitative trait loci (QTLs) as an unobserved random variable and using ideas similar to composite interval mapping to account for the effects of QTLs in other chromosomes. The method is introduced for inbred lines and it can be applied also in situations involving frequent missing genotypes. We propose that two new probabilistic measures be used to summarize the results from the statistical analysis: (1) the (posterior) QTL intensity, for estimating the number of QTLs in a chromosome and for localizing them into some particular chromosomal regions, and (2) the locationwise (posterior) distributions of the phenotypic effects of the QTLs. Both these measures will be viewed as functions of the putative QTL locus, over the marker range in the linkage group. The method is tested and compared with standard interval and composite interval mapping techniques by using simulated backcross progeny data. It is implemented as a software package. Its initial version is freely available for research purposes under the name Multimapper at URL http://www.rni.helsinki.fi/mjs.

Bayes Theorem↗
PubMed2000

Air quality and pediatric emergency room visits for asthma in Atlanta, Georgia, USA.

Pediatric emergency room visits for asthma were studied in relation to air quality indices in a spatio-temporal investigation of approximately 130,000 visits (approximately 6,000 for asthma) to the major emergency care centers in Atlanta, Georgia, during the summers of 1993-1995. Generalized estimating equations, logistic regression, and Bayesian models were fitted to the data. In logistic regression models comparing estimated exposures of asthma cases with those of the nonasthma patients, controlling for temporal and demographic covariates and using residential zip code to link patients to spatially resolved ozone levels, the estimated relative risk per 20 parts per billion (ppb) increase in the maximum 8-hour ozone level was 1.04 (p < 0.05). The estimated relative risk for particulate matter less than or equal to 10 microm in aerodynamic diameter (PM10) was 1.04 per 15 microg/m3 (p < 0.05). Exposure-response trends (p < 0.01) were observed for ozone (>100 ppb vs. <50 ppb: odds ratio = 1.23, p = 0.003) and PM10 (>60 microg/m3 vs. <20 microg/m3: odds ratio = 1.26, p = 0.004). In models with ozone and PM10, both terms became nonsignificant because of collinearity of the variables (r= 0.75). The other analytical approaches yielded consistent findings. This study supports accumulating evidence regarding the relation of air pollution to childhood asthma exacerbation.

Adolescent↗
PubMed1989

Evaluation of a two-compartment Bayesian forecasting program for predicting vancomycin concentrations.

The application of a two-compartment Bayesian forecasting program for vancomycin was tested retrospectively in 45 adult patients with stable renal function. Serial blood samples from 25 of these patients were used to determine population-based parameter estimates. The predictive performance of the Bayesian program was assessed by using both non-steady-state and steady-state vancomycin concentrations as feedback information. Overall, the program tended to underpredict peak and trough steady-state vancomycin serum concentrations. A larger mean prediction error (ME) was seen when non-steady-state feedback serum concentrations were used compared with using population-based parameter estimates (no feedback). In contrast, a marked improvement in ME (peaks: -1.03 versus -2.61; troughs: -1.60 versus -2.07) was seen when steady-state feedback serum concentrations were used compared with no feedback data. Precision improved when either feedback serum concentrations were used to predict steady-state peak and trough vancomycin concentrations. The results from this clinical evaluation demonstrate that the initial pharmacokinetic parameter estimates for a two-compartment Bayesian model provided accurate prediction of steady-state vancomycin concentrations. Prediction bias and precision were improved when steady-state vancomycin concentrations were used to determine individualized pharmacokinetic parameters.

Adult↗
PubMed1992

Predictive performance of Bayesian and nonlinear least-squares regression programs for lidocaine.

The predictive performance of two computer programs for lidocaine dosing were evaluated. Two-compartment Bayesian and nonlinear least-squares regression programs were used in two groups of patients (15 acute arrhythmia patients and 14 chronic arrhythmia patients). Lidocaine was given as a 1.5 mg/kg bolus and a 2.8 mg/min infusion for 48 h. A second bolus (0.5 mg/kg) was given 10 min after the first bolus over 2 min. Serum samples of the patients receiving lidocaine were drawn at 2, 15, 30 min and 1, 2, and 4 h and were used in forecasting the serum concentrations at 6, 8, 12, and 48 h. Predictive performance was assessed by mean error and mean-squared error. The results (mean +/- 95% confidence intervals) demonstrated the Bayesian program predicted a significant (p less than 0.05) difference at 12 h between the two arrhythmia groups (acute 0.52 [-0.95; -0.09] and chronic 0.28 [0.12; 0.44]). The results also demonstrated the Bayesian method was significantly more precise compared to the nonlinear least-squares regression program at 8, 12, and 48 h for the acute group. While caution is warranted, this study demonstrated that the predictive performance by a two-compartment Bayesian model is more accurate in predicting future lidocaine serum concentrations than that by nonlinear least-squares regression.

Acute Disease↗
PubMed2004

Small-area incidence trends in breast cancer.

BACKGROUND: During the past 2 decades, the observed incidence of in situ and early-stage invasive breast cancer has increased substantially as a result of increased use of mammography. Geographic variability in the increase in breast cancer incidence has been observed among large areas. Examining the variability among small areas in the incidence over time will facilitate appropriate geographic allocation of resources aimed at increasing screening. METHODS: We examined county-specific increases in breast cancer incidence over time, specifically the variability and spatial correlation in the increase in breast cancer incidence. The analyses were based on county-level data (1973-1997) from the Iowa Surveillance, Epidemiology, and End Results program. A spatiotemporal hierarchical Bayesian model was used to examine variability in county-specific rates (intercepts, slopes, and spatial correlations) among white women at least 40 years of age. RESULTS: Posterior values indicate there was little variability among counties in the change in breast cancer incidence over time (slope) but substantial variation among intercepts. There was considerable spatial correlation among the county-specific intercepts but a lack of a spatial correlation among the county-specific slopes. There was no correlation between the county-specific intercept and slope. CONCLUSIONS: Breast cancer incidence increased over time, but county-specific rates increased independently relative to their neighboring counties or their initial rate.

Adult↗
PubMed1998

Biases in three-dimensional structure-from-motion arise from noise in the early visual system.

The projected pattern of retinal-image motion supplies the human visual system with valuable information about properties of the three-dimensional environment. How well three-dimensional properties can be recovered depends both on the accuracy with which the early motion system estimates retinal motion, and on the way later processes interpret this retinal motion. Here we combine both early and late stages of the computational process to account for the hitherto puzzling phenomenon of systematic biases in three-dimensional shape perception. We present data showing how the perceived depth of a hinged plane ('an open book') can be systematically biased by the extent over which it rotates. We then present a Bayesian model that combines early measurement noise with geometric reconstruction of the three-dimensional scene. Although this model has no in-built bias towards particular three-dimensional shapes, it accounts for the data well. Our analysis suggests that the biases stem largely from the geometric constraints imposed on what three-dimensional scenes are compatible with the (noisy) early motion measurements. Given these findings, we suggest that the visual system may act as an optimal estimator of three-dimensional structure-from-motion.

Computer Simulation↗