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PubMed2002

Effects of systematic exposure assessment errors in partially ecologic case-control studies.

BACKGROUND: In ecologic studies, group-level rather than individual-level exposure data are used. When using group-level exposure data, established by sufficiently large samples of individual exposure assessments, the bias of the effect estimate due to sampling errors or random assessment errors at the individual-level is generally negligible. In contrast, systematic assessment errors may produce more pronounced errors in the group-level exposure measures, leading to bias in ecologic analyses. METHODS: We focus on effects of systematic exposure assessment errors in partially ecologic case-control studies. Individual-level information on disease status, group membership, and covariates is obtained from registries, whereas the exposure is a group-level measure obtained from an established exposure database. Effects on bias and coverage of 95% CI in various error situations are investigated under the linear risk model, using both simulated and empirical ecologic data on exposures that are binary at the individual level. RESULTS: Our simulations suggest that the bias produced by systematic exposure assessment errors under the linear risk model is generally approximately equal to the ratio of the slope bias and the intercept bias in ordinary linear regression with measurement errors in the independent variable. Consequently, bias in either direction can occur. Exposure assessment errors that systematically distort the group-level exposure measures have more pronounced effects on bias and coverage than errors producing random fluctuations of the group-level measures, which imply bias towards the null. CONCLUSIONS: The results indicate the need for careful consideration of potential effects of systematic distortions of the group-level exposure measures when constructing and applying group-level exposure databases, such as probabilistic job exposure matrices.

Bias↗
PubMed2005

Genetic Interaction Motif Finding by expectation maximization--a novel statistical model for inferring gene modules from synthetic lethality.

BACKGROUND: Synthetic lethality experiments identify pairs of genes with complementary function. More direct functional associations (for example greater probability of membership in a single protein complex) may be inferred between genes that share synthetic lethal interaction partners than genes that are directly synthetic lethal. Probabilistic algorithms that identify gene modules based on motif discovery are highly appropriate for the analysis of synthetic lethal genetic interaction data and have great potential in integrative analysis of heterogeneous datasets. RESULTS: We have developed Genetic Interaction Motif Finding (GIMF), an algorithm for unsupervised motif discovery from synthetic lethal interaction data. Interaction motifs are characterized by position weight matrices and optimized through expectation maximization. Given a seed gene, GIMF performs a nonlinear transform on the input genetic interaction data and automatically assigns genes to the motif or non-motif category. We demonstrate the capacity to extract known and novel pathways for Saccharomyces cerevisiae (budding yeast). Annotations suggested for several uncharacterized genes are supported by recent experimental evidence. GIMF is efficient in computation, requires no training and automatically down-weights promiscuous genes with high degrees. CONCLUSION: GIMF effectively identifies pathways from synthetic lethality data with several unique features. It is mostly suitable for building gene modules around seed genes. Optimal choice of one single model parameter allows construction of gene networks with different levels of confidence. The impact of hub genes the generic probabilistic framework of GIMF may be used to group other types of biological entities such as proteins based on stochastic motifs. Analysis of the strongest motifs discovered by the algorithm indicates that synthetic lethal interactions are depleted between genes within a motif, suggesting that synthetic lethality occurs between-pathway rather than within-pathway.

Algorithms↗
PubMed1999

Naive probability: a mental model theory of extensional reasoning.

This article outlines a theory of naive probability. According to the theory, individuals who are unfamiliar with the probability calculus can infer the probabilities of events in an extensional way: They construct mental models of what is true in the various possibilities. Each model represents an equiprobable alternative unless individuals have beliefs to the contrary, in which case some models will have higher probabilities than others. The probability of an event depends on the proportion of models in which it occurs. The theory predicts several phenomena of reasoning about absolute probabilities, including typical biases. It correctly predicts certain cognitive illusions in inferences about relative probabilities. It accommodates reasoning based on numerical premises, and it explains how naive reasoners can infer posterior probabilities without relying on Bayes's theorem. Finally, it dispels some common misconceptions of probabilistic reasoning.

Cognition↗
PubMedDate not supplied

[The theory of functional systems and purposeful behavior].

The role of probability forecasting in the purposive behavior under conditions of subjective uncertainty is considered in terms of the theory of functional systems. Participation of the probability forecasting in the afferent synthesis, goal formation, formation of the acceptor of action result and action program, and, finally, in the action program actualization is substantiated. The model of behavior under conditions of subjective uncertainty is advanced. It includes all the classical elements of the model of behavioral act developed by P.K. Anokhin. In order to take into account the probability aspects of behavior, the role of probability forecasting is emphasized at every stage of the system functioning. In addition to the classical elements, two novel components are introduced. These are the "memory buffer" (results of searching reactions) and the apparatus of probability decisions about changes in the action program. By the memory buffer an apparatus is meant, which gathers and stores the information about the results of many behavioral acts performed during the actualization of the action program. This information is used in the process of making a probability decision as whether to alter or not the action program after each specific behavioral act. Such an approach integrates the probability forecasting and the theory of functional systems. The theory becomes universal, i.e., applicable not only to deterministic but also to probabilistic environments.

Animals↗
PubMed1998

An automatic diagnostic system for CT liver image classification.

Computed tomography (CT) images have been widely used for liver disease diagnosis. Designing and developing computer-assisted image processing techniques to help doctors improve their diagnosis has received considerable interests over the past years. In this paper, a CT liver image diagnostic classification system is presented which will automatically find, extract the CT liver boundary and further classify liver diseases. The system comprises a detect-before-extract (DBE) system which automatically finds the liver boundary and a neural network liver classifier which uses specially designed feature descriptors to distinguish normal liver, two types of liver tumors, hepatoma and hemageoma. The DBE system applies the concept of the normalized fractional Brownian motion model to find an initial liver boundary and then uses a deformable contour model to precisely delineate the liver boundary. The neural network is included to classify liver tumors into hepatoma and hemageoma. It is implemented by a modified probabilistic neural network (PNN) [MPNN] in conjunction with feature descriptors which are generated by fractal feature information and the gray-level co-occurrence matrix. The proposed system was evaluated by 30 liver cases and shown to be efficient and very effective.

Algorithms↗
PubMed2005

Stochastic analysis to assess the spatial distribution of groundwater nitrate concentrations in the Po catchment (Italy).

A large database including temporal trends of physical, ecological and socio-economic data was developed within the EUROCAT project. The aim was to estimate the nutrient fluxes for different socio-economic scenarios at catchment and coastal zone level of the Po catchment (Northern Italy) with reference to the Water Quality Objectives reported in the Water Framework Directive (WFD 2000/60/CE) and also in Italian legislation. Emission data derived from different sources at national, regional and local levels are referred to point and non-point sources. While non-point (diffuse) sources are simply integrated into the nutrient flux model, point sources are irregularly distributed. Intensive farming activity in the Po valley is one of the main Pressure factors Driving groundwater pollution in the catchment, therefore understanding the spatial variability of groundwater nitrate concentrations is a critical issue to be considered in developing a Water Quality Management Plan. In order to use the scattered point source data as input in our biogeochemical and transport models, it was necessary to predict their values and associated uncertainty at unsampled locations. This study reports the spatial distribution and uncertainty of groundwater nitrate concentration at a test site of the Po watershed using a probabilistic approach. Our approach was based on geostatistical sequential Gaussian simulation used to yield a series of stochastic images characterized by equally probable spatial distributions of the nitrate concentration across the area. Post-processing of many simulations allowed the mapping of contaminated and uncontaminated areas and provided a model for the uncertainty in the spatial distribution of nitrate concentrations.

Agriculture↗
PubMed1980

Semantic priming in nonproductive schizophrenia.

A group of 10 nonproductive schizophrenics (i.e., not experiencing hallucinations, delusions, or mood swings) was compared with a group of 10 nonpsychotic alcoholics, matched on visual acuity and reading level, in a primed word identification task. Subsets of three target words were projected onto a series of screens constructed in such a way that an increasing random proportion of the words' features were made visible. The prime was the category to which the target words belonged; the target words represented one of four levels of goodness-of-membership in the primed category. On the whole, identification thresholds in the two groups were not significantly different. However, although the alcoholics' thresholds increased rapidly as goodness-of-membership decreased, this trend was not significant in the schizophrenic group. When the target words were poor members of the primed category, the schizophrenics' thresholds were significantly lower than the alcoholics'. Such results are consistent with Feigenberg's characterization of schizophrenia as a disturbance in the process of probabilistic prognosis.

Alcoholism↗
PubMed2006

H-CORE: enabling genome-scale Bayesian analysis of biological systems without prior knowledge.

The Bayesian network is a popular tool for describing relationships between data entities by representing probabilistic (in)dependencies with a directed acyclic graph (DAG) structure. Relationships have been inferred between biological entities using the Bayesian network model with high-throughput data from biological systems in diverse fields. However, the scalability of those approaches is seriously restricted because of the huge search space for finding an optimal DAG structure in the process of Bayesian network learning. For this reason, most previous approaches limit the number of target entities or use additional knowledge to restrict the search space. In this paper, we use the hierarchical clustering and order restriction (H-CORE) method for the learning of large Bayesian networks by clustering entities and restricting edge directions between those clusters, with the aim of overcoming the scalability problem and thus making it possible to perform genome-scale Bayesian network analysis without additional biological knowledge. We use simulations to show that H-CORE is much faster than the widely used sparse candidate method, whilst being of comparable quality. We have also applied H-CORE to retrieving gene-to-gene relationships in a biological system (The 'Rosetta compendium'). By evaluating learned information through literature mining, we demonstrate that H-CORE enables the genome-scale Bayesian analysis of biological systems without any prior knowledge.

Algorithms↗
PubMed2004

Development of probabilistic emission inventories of air toxics for Jacksonville, Florida, USA.

Probabilistic emission inventories were developed for 1,3-butadiene, mercury (Hg), arsenic (As), benzene, formaldehyde, and lead for Jacksonville, FL. To quantify inter-unit variability in empirical emission factor data, the Maximum Likelihood Estimation (MLE) method or the Method of Matching Moments was used to fit parametric distributions. For data sets that contain nondetected measurements, a method based upon MLE was used for parameter estimation. To quantify the uncertainty in urban air toxic emission factors, parametric bootstrap simulation and empirical bootstrap simulation were applied to uncensored and censored data, respectively. The probabilistic emission inventories were developed based on the product of the uncertainties in the emission factors and in the activity factors. The uncertainties in the urban air toxics emission inventories range from as small as -25 to +30% for Hg to as large as -83 to +243% for As. The key sources of uncertainty in the emission inventory for each toxic are identified based upon sensitivity analysis. Typically, uncertainty in the inventory of a given pollutant can be attributed primarily to a small number of source categories. Priorities for improving the inventories and for refining the probabilistic analysis are discussed.

Air Pollutants↗
PubMed2006

Differentiation of sCJD and vCJD forms by automated analysis of basal ganglia intensity distribution in multisequence MRI of the brain--definition and evaluation of new MRI-based ratios.

We present a method for the analysis of basal ganglia (including the thalamus) for accurate detection of human spongiform encephalopathy in multisequence magnetic resonance imaging (MRI) of the brain. One common feature of most forms of prion protein diseases is the appearance of hyperintensities in the deep grey matter area of the brain in T2-weighted magnetic resonance (MR) images. We employ T1, T2, and Flair-T2 MR sequences for the detection of intensity deviations in the internal nuclei. First, the MR data are registered to a probabilistic atlas and normalized in intensity. Then smoothing is applied with edge enhancement. The segmentation of hyperintensities is performed using a model of the human visual system. For more accurate results, a priori anatomical data from a segmented atlas are employed to refine the registration and remove false positives. The results are robust over the patient data and in accordance with the clinical ground truth. Our method further allows the quantification of intensity distributions in basal ganglia. The caudate nuclei are highlighted as main areas of diagnosis of sporadic Creutzfeldt-Jakob Disease (sCJD), in agreement with the histological data. The algorithm permitted the classification of the intensities of abnormal signals in sCJD patient FLAIR images with a higher hypersignal in caudate nuclei (10/10) and putamen (6/10) than in thalami. Defining normalized MRI measures of the intensity relations between the internal grey nuclei of patients, we robustly differentiate sCJD and variant CJD (vCJD) patients, in an attempt to create an automatic classification tool of human spongiform encephalopathies.

Algorithms↗
PubMedDate not supplied

Are normative expert systems appropriate for diagnostic pathology?

Conceptual models for diagnostic reasoning proposed in the medical literature are presented to stimulate discussion about the issue of the appropriateness of probabilistic knowledge-based systems for medical diagnosis. Evidence is presented to corroborate the authors' view that diagnosis is a problem-solving task, rather than a decision-making task. In the authors' opinion, probabilistic reasoning is better suited to situations dealing with choices for clinical intervention, rather than to those dealing with determining the correct diagnosis. A critique is given of a diagnostic Bayesian expert system for lymph node pathology. In empirical studies, diagnostic Bayesian systems have been shown to typically list the correct diagnosis as the program's first choice 60% to 70% of the time. One reason for this undistinguished level of diagnostic performance is that Bayesian systems are not designed to represent and use knowledge the same way that an expert does.

Bayes Theorem↗
PubMed2001

Redundancy reduction revisited.

Soon after Shannon defined the concept of redundancy it was suggested that it gave insight into mechanisms of sensory processing, perception, intelligence and inference. Can we now judge whether there is anything in this idea, and can we see where it should direct our thinking? This paper argues that the original hypothesis was wrong in over-emphasizing the role of compressive coding and economy in neuron numbers, but right in drawing attention to the importance of redundancy. Furthermore there is a clear direction in which it now points, namely to the overwhelming importance of probabilities and statistics in neuroscience. The brain has to decide upon actions in a competitive, chance-driven world, and to do this well it must know about and exploit the non-random probabilities and interdependences of objects and events signalled by sensory messages. These are particularly relevant for Bayesian calculations of the optimum course of action. Instead of thinking of neural representations as transformations of stimulus energies, we should regard them as approximate estimates of the probable truths of hypotheses about the current environment, for these are the quantities required by a probabilistic brain working on Bayesian principles.

Artificial Intelligence↗
PubMed2003

[Prevalence of serious mental pathology in the city of San Luis].

OBJECTIVE: This work includes part of the results of the first epidemiological research on mental health held at San Luis City, aimed to estimate adult prevalence rate of mental pathology on San Luis' adult population. It has also been considered the prevalent kind of disorders. In both cases, it has been taken into account five domains of independent variables, due to sociodemographic characteristics: sex, age, educational level, occupational level, and residence zone. METHODS: The methodology is based on the epidemiological descriptive and transversal model. The assessment instrument, the Millon Clinical Multiaxial Inventory-II, was administrated to a representative adult sample, designed by a probabilistic sample methodology. RESULTS: The clinical measurement of Personality Pathology Scales and Sever Clinical Syndromes Scales showed a prevalence rate of 25,69% of serious mental pathology in general population, which is higher in women, 27,13%; than in men, 24,19%. In accordance to independent variables, there is a higher prevalence in elderly people, although there is no correlation with the number of symptoms; there is a lower prevalence among people with more years of education, with a relative correlation with number of symptoms-; and some professions showed higher scores than others, also with a relative correlation with number of symptoms. Considering the kind of mental disorders, there was a high prevalence of paranoid disorder and of alucinatory disorder, both in the women and men's samples. CONCLUSIONS: Finally, we found that the prevalence rates of serious mental pathology in San Luis population are similar to those obtained by previous studies in Argentina.

Adolescent↗
PubMed1992

Long-term potentiation is associated with increases in quantal content and quantal amplitude.

Long-term potentiation (LTP) of synaptic transmission in CA1 neurons of the hippocampus, elicited by the conjunction of presynaptic firing and postsynaptic depolarization, is an important model of plasticity, which may underlie memory storage. Although induction of LTP takes place in the postsynaptic cell, it is not clear whether it is expressed through an enhancement of transmitter release or through an increased postsynaptic response to the same amount of transmitter. Analysis of the trial-to-trial amplitude fluctuations of synaptic signals, that is quantal analysis, gives an important insight into the probabilistic mechanisms of transmission, although attempts to apply it to the mode of expression of LTP have so far yielded inconsistent results, at least in part because they have relied on models of transmitter release that have not been confirmed experimentally. Here we report clear evidence for quantal fluctuation in a subset of cells. Induction of LTP in these cells causes abrupt increases in either quantal content or quantal amplitude, or both. This shows that two different mechanisms can underlie the maintenance of LTP.

Animals↗
PubMed1992

Combining exemplar-based category representations and connectionist learning rules.

Adaptive network and exemplar-similarity models were compared on their ability to predict category learning and transfer data. An exemplar-based network (Kruschke, 1990a, 1990b, 1992) that combines key aspects of both modeling approaches was also tested. The exemplar-based network incorporates an exemplar-based category representation in which exemplars become associated to categories through the same error-driven, interactive learning rules that are assumed in standard adaptive networks. Experiment 1, which partially replicated and extended the probabilistic classification learning paradigm of Gluck and Bower (1988a), demonstrated the importance of an error-driven learning rule. Experiment 2, which extended the classification learning paradigm of Medin and Schaffer (1978) that discriminated between exemplar and prototype models, demonstrated the importance of an exemplar-based category representation. Only the exemplar-based network accounted for all the major qualitative phenomena; it also achieved good quantitative predictions of the learning and transfer data in both experiments.

Concept Formation↗
PubMed1997

Estimating the probability of acute appendicitis using clinical criteria of a structured record sheet: the physician against the computer.

OBJECTIVE: To evaluate physicians' probability estimates of acute appendicitis based on structured collection of clinical data. DESIGN: Open prospective study. SETTING: District hospital, Norway. SUBJECTS: 304 patients admitted with suspected acute appendicitis. INTERVENTIONS: Initial diagnostic accuracy of physicians was compared with corresponding results from a computer model. MAIN OUTCOME MEASURES: The estimated probabilities of appendicitis in different testing groups were analysed using receiver operating characteristic (ROC) curves. RESULTS: Physicians' estimates had a mean area under ROC-curve of 0.81 (95% CI 0.79 to 0.82), not significantly different from the computer model. Both correlated well with the actual rate of appendicitis, but the physicians tended to overestimate the probability by 10%. CONCLUSION: Physicians' probability estimates perform rather well. Further attempts to implement a probabilistic approach in the diagnostic process of acute appendicitis therefore seem justified.

Acute Disease↗
PubMed2003

General-purpose computation with neural networks: a survey of complexity theoretic results.

We survey and summarize the literature on the computational aspects of neural network models by presenting a detailed taxonomy of the various models according to their complexity theoretic characteristics. The criteria of classification include the architecture of the network (feedforward versus recurrent), time model (discrete versus continuous), state type (binary versus analog), weight constraints (symmetric versus asymmetric), network size (finite nets versus infinite families), and computation type (deterministic versus probabilistic), among others. The underlying results concerning the computational power and complexity issues of perceptron, radial basis function, winner-take-all, and spiking neural networks are briefly surveyed, with pointers to the relevant literature. In our survey, we focus mainly on the digital computation whose inputs and outputs are binary in nature, although their values are quite often encoded as analog neuron states. We omit the important learning issues.

Algorithms↗
PubMed2005

Randomized algorithms for stability and robustness analysis of high-speed communication networks.

This paper initiates a study toward developing and applying randomized algorithms for stability of high-speed communication networks. The focus is on congestion and delay-based flow controllers for sources, which are "utility maximizers" for individual users. First, we introduce a nonlinear algorithm for such source flow controllers, which uses as feedback aggregate congestion and delay information from bottleneck nodes of the network, and depends on a number of parameters, among which are link capacities, user preference for utility, and pricing. We then linearize this nonlinear model around its unique equilibrium point and perform a robustness analysis for a special symmetric case with a single bottleneck node. The "symmetry" here captures the scenario when certain utility and pricing parameters are the same across all active users, for which we derive closed-form necessary and sufficient conditions for stability and robustness under parameter variations. In addition, the ranges of values for the utility and pricing parameters for which stability is guaranteed are computed exactly. These results also admit counterparts for the case when the pricing parameters vary across users, but the utility parameter values are still the same. In the general nonsymmetric case, when closed-form derivation is not possible, we construct specific randomized algorithms which provide a probabilistic estimate of the local stability of the network. In particular, we use Monte Carlo as well as quasi-Monte Carlo techniques for the linearized model. The results obtained provide a complete analysis of congestion control algorithms for internet style networks with a single bottleneck node as well as for networks with general random topologies.

Algorithms↗

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