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PubMed2006

Mutagenic probability estimation of chemical compounds by a novel molecular electrophilicity vector and support vector machine.

MOTIVATION: Mutagenicity is among the toxicological end points that pose the highest concern. The accelerated pace of drug discovery has heightened the need for efficient prediction methods. Currently, most available tools fall short of the desired degree of accuracy, and can only provide a binary classification. It is of significance to develop a discriminative and informative model for the mutagenicity prediction. RESULTS: Here we developed a mutagenic probability prediction model addressing the problem, based on datasets covering a large chemical space. A novel molecular electrophilicity vector (MEV) is first devised to represent the structure profile of chemical compounds. An extended support vector machine (SVM) method is then used to derive the posterior probabilistic estimation of mutagenicity from the MEVs of the training set. The results show that our model gives a better performance than TOPKAT (http://www.accelrys.com) and other previously published methods. In addition, a confidence level related to the prediction can be provided, which may help people make more flexible decisions on chemical ordering or synthesis. AVAILABILITY: The binary program (ZGTOX_1.1) based on our model and samples of input datasets on Windows PC are available at http://dddc.ac.cn/adme upon request from the authors.

Algorithms↗
PubMed2002

Teaching and learning in a probabilistic prisoner's dilemma.

The prisoner's dilemma is much studied in social psychology and decision-making because it models many real-world conflicts. In everyday terms, the choice to 'cooperate' (maximize reward for the group) or 'defect' (maximize reward for the individual) is often attributed to altruistic or selfish motives. Alternatively, behavior during a dilemma may be understood as a function of reinforcement and punishment. Human participants played a prisoner's-dilemma-type game (for points exchangeable for money) with a computer that employed either a teaching strategy (a probabilistic version of tit-for-tat), in which the computer reinforced or punished participants' cooperation or defection, or a learning strategy (a probabilistic version of Pavlov), in which the computer's responses were reinforced and punished by participants' cooperation and defection. Participants learned to cooperate against both computer strategies. However, in a second experiment which varied the context of the game, they learned to cooperate only against one or other strategy; participants did not learn to cooperate against tit-for-tat when they believed that they were playing against another person; participants did not learn to cooperate against Pavlov when the computer's cooperation probability was signaled by a spinner. The results are consistent with the notion that people are biased not only to cooperate or defect on individual social choices, but also to employ one or other strategy of interaction in a pattern across social choices.

Journal Article↗
PubMed2006

Probabilistic mechanisms in sensorimotor control.

Uncertainty constitutes a fundamental constraint on human sensorimotor control. Our sensors are noisy and do not provide perfect information about all the properties of the world. Moreover, our muscles generate noisy outputs and many tasks we perform vary in an unpredictable way. Here we review the computations that the CNS uses in the face of such sensory, motor and task uncertainty. We show that the CNS reduces the uncertainty in estimates about the state of the world by using a Bayesian combination of prior knowledge and sensory feedback. It is shown that these mechanisms generalize to state estimation of ones own body during movement. We review how the CNS optimizes decisions based on these estimates, examining the error criterion that people optimize when performing targeted movements. Finally, we describe how signal-dependent noise on the motor output places constraints on performance. Goal-directed movement arises from a model in which the statistics of our actions are optimized. Together these studies provide a probabilistic framework for sensorimotor control.

Animals↗
PubMed1982

Higher inductions of twin and single sister chromatid exchanges by cross-linking agents in Fanconi's anemia cells.

Twin and single sister chromatid exchanges (SCEs) induced by short treatments with mitomycin C (MC) and 4,5',8-trimethylpsoralen (TMP)-plus-near ultraviolet light (NUV) were analyzed in colcemid-induced endoreduplicated normal human and typical Fanconi's anemia (FA) fibroblasts with diplochromosomes. The induction rate of twin SCEs that had occurred in the first cycle (S1) after the treatment was 1.7--2.4 times higher in FA cells than in normal cells. The induction rate of single SCEs that had arisen during the second cycle (S2) long after the treatment was also much higher, though less than the twin SCE rate, in FA cells than the almost negligible rate after repair of cross-links and monoadducts in normal cells. These results in FA cells, which specifically lack the first half-excision step of the two-step cross-link repair but retain the normal monoadduct repair, indicate that MC or TMP cross-links remaining unrepaired are indeed responsible for higher inductions of twin (S1 exchange) and single SCEs (S2 exchange). Thus, these findings indicate that Shafer's model of replication bypass for cross-link-induced SCE, which predicts greatly reduced twin SCE formation in FA cells due to half cancellation, is apparently inadequate as such. We present three plausible models, incorporating the ordinary replication model, random unilateral cross-link transfer, and chromatid breakage/reunion, that can account for the probabilistic inductions of single and twin SCEs and even for no SCE formation.

Anemia, Aplastic↗
PubMedDate not supplied

Gaussian process modeling in conjunction with individual patient simulation modeling: a case study describing the calculation of cost-effectiveness ratios for the treatment of established osteoporosis.

Individual patient-level models can simulate more complex disease processes than cohort-based approaches. However, large numbers of patients need to be simulated to reduce 1st-order uncertainty, increasing the computational time required and often resulting in the inability to perform extensive sensitivity analyses. A solution, employing Gaussian process techniques, is presented using a case study, evaluating the cost-effectiveness of a sample of treatments for established osteoporosis. The Gaussian process model accurately formulated a statistical relationship between the inputs to the individual patient model and its outputs. This model reduced the time required for future runs from 150 min to virtually-instantaneous, allowing probabilistic sensitivity analyses-to be undertaken. This reduction in computational time was achieved with minimal loss in accuracy. The authors believe that this case study demonstrates the value of this technique in handling 1st- and 2nd-order uncertainty in the context of health economic modeling, particularly when more widely used techniques are computationally expensive or are unable to accurately model patient histories.

Alendronate↗
PubMed2005

Efficient decoding algorithms for generalized hidden Markov model gene finders.

BACKGROUND: The Generalized Hidden Markov Model (GHMM) has proven a useful framework for the task of computational gene prediction in eukaryotic genomes, due to its flexibility and probabilistic underpinnings. As the focus of the gene finding community shifts toward the use of homology information to improve prediction accuracy, extensions to the basic GHMM model are being explored as possible ways to integrate this homology information into the prediction process. Particularly prominent among these extensions are those techniques which call for the simultaneous prediction of genes in two or more genomes at once, thereby increasing significantly the computational cost of prediction and highlighting the importance of speed and memory efficiency in the implementation of the underlying GHMM algorithms. Unfortunately, the task of implementing an efficient GHMM-based gene finder is already a nontrivial one, and it can be expected that this task will only grow more onerous as our models increase in complexity. RESULTS: As a first step toward addressing the implementation challenges of these next-generation systems, we describe in detail two software architectures for GHMM-based gene finders, one comprising the common array-based approach, and the other a highly optimized algorithm which requires significantly less memory while achieving virtually identical speed. We then show how both of these architectures can be accelerated by a factor of two by optimizing their content sensors. We finish with a brief illustration of the impact these optimizations have had on the feasibility of our new homology-based gene finder, TWAIN. CONCLUSIONS: In describing a number of optimizations for GHMM-based gene finders and making available two complete open-source software systems embodying these methods, it is our hope that others will be more enabled to explore promising extensions to the GHMM framework, thereby improving the state-of-the-art in gene prediction techniques.

Algorithms↗
PubMed2000

Inference of population structure using multilocus genotype data.

We describe a model-based clustering method for using multilocus genotype data to infer population structure and assign individuals to populations. We assume a model in which there are K populations (where K may be unknown), each of which is characterized by a set of allele frequencies at each locus. Individuals in the sample are assigned (probabilistically) to populations, or jointly to two or more populations if their genotypes indicate that they are admixed. Our model does not assume a particular mutation process, and it can be applied to most of the commonly used genetic markers, provided that they are not closely linked. Applications of our method include demonstrating the presence of population structure, assigning individuals to populations, studying hybrid zones, and identifying migrants and admixed individuals. We show that the method can produce highly accurate assignments using modest numbers of loci-e.g. , seven microsatellite loci in an example using genotype data from an endangered bird species. The software used for this article is available from http://www.stats.ox.ac.uk/ approximately pritch/home. html.

Algorithms↗
PubMedDate not supplied

Reliability of cognitive tests used in Alzheimer's disease.

Assessment of cognitive status is a key component of monitoring Alzheimer's patients during the course of their illness. The reliability of a cognitive test is a measure of its reproducibility under replicate conditions. In the classical setting, reliability is defined in three ways: the ratio of the variance of the true scores to the variance of the observed scores; the correlation of observed scores on two parallel forms of the test, and the square of the correlation between the observed score and the true score. In the classical case of independence of true scores and measurement errors, the three definitions are equivalent. Estimation of reliability through analysis of variance techniques and construction of confidence intervals is accomplished when the true scores and errors are normally distributed. This paper examines a non-parametric, probabilistic estimate of reliability as the probability that, given a parallel test, the second set of scores has the same ranking as the first set. In the classical case there is a monotonic relationship between this measure and the reliability. This measure is also linked to Kendall's tau. The performance of the probabilistic measure is compared with the traditional measures in a variety of models, including those with bounded scales, and those with skewed distributions. The ideas are extended to the case of the reliability of change scores and to biased estimators of true scores. In this context truncation models and Bayes estimates of true scores are considered.

Aged↗
PubMed2001

Location-probability profiles for the mouth region of human primary motor-sensory cortex: model and validation.

The mouth representation of the human, primary motor cortex (M1) is not reliably identified by surface anatomy but may be reliably localized by means of spatial coordinates. For this report, three quantitative metanalyses were performed which jointly described the mean location, location variability and location-probability profiles of the human M1-mouth representation. First, a literature metanalysis of intersubject functional-area variability was performed using eleven, per-subject studies, each of which reported a coordinate-referenced measure of intersubject variability for one or more brain areas. From these data, a weighted-mean value for intersubject variability was computed, which proved to be small (5.6 mm, standard deviation), consistent across coordinate axes (x, y, z), and consistent across brain areas. Second, a literature metanalysis of the location of M1-mouth was performed using seven, coordinate-referenced, group-mean studies (71 subjects in all), each of which reported a grand-average location for M1-mouth. From this, a weighted-mean location and weighted values for total variability (interlaboratory plus interindividual) were determined. Using these two literature metanalyses as input data, location-probability profiles were computed for the cardinal axes (x, y, and z) of the reference space, using the functional volumes modeling (FVM) statistical model. Third, an original-data metanalysis was performed on in-house PET data from 30 normal subjects performing overt-speech tasks. M1-mouth's mean location, location variability, and location-probability profiles were consistent with those conjointly modeled by FVM from the two literature metanalyses. Collectively, these observations provide a detailed, consensus probabilistic description of the location of the human M1-mouth representation in standardized coordinates.

Adult↗
PubMed1992

Stochastic population forecasts and their uses.

"The properties and uses of stochastic forecasts are discussed here. For linear stochastic projections, we show how the computation of forecast moments and the statistical distribution of forecasts depend on the multiplicative and autoregressive structure of the dynamics. Both scalar and vector projection methods are discussed, and their similarities are explored. Next we discuss the uses of stochastic forecasts, arguing that it is important to relate forecasts to the specific decision-making criteria of particular forecast users. The example of [the U.S. system of] Social Security is used to show how a dynamic programming approach may be used to explore alternative decisions in a probabilistic context."

Americas↗
PubMed2000

Delayed stochastic systems

Noise and time delay are two elements that are associated with many natural systems, and often they are sources of complex behaviors. Understanding of this complexity is yet to be explored, particularly when both elements are present. As a step to gain insight into such complexity for a system with both noise and delay, we investigate such delayed stochastic systems both in dynamical and probabilistic perspectives. A Langevin equation with delay and a random-walk model whose transition probability depends on a fixed time-interval past (delayed random walk model) are the subjects of in depth focus. As well as considering relations between these two types of models, we derive an approximate Fokker-Planck equation for delayed stochastic systems and compare its solution with numerical results.

Journal Article↗
PubMed2002

A quantitative risk analysis tool for design/simulation of wastewater treatment plants.

Uncertainty is a central concept in the decision-making process, especially when dealing with biological systems subject to large natural variations. In the design of activated sludge systems, a conventional approach in dealing with uncertainty is implicitly translating it into above-normal safety factors, which in some cases may even increase the capital investments by an order of magnitude. To obviate this problem, an alternative design approach explicitly incorporating uncertainty is herein proposed. A probabilistic Monte Carlo engine is coupled to deterministic wastewater treatment plant (WWTP) models. The paper provides a description of the approach and a demonstration of the general adequacy of the method. The procedure is examined in an upgrade of a conventional WWTP towards stricter effluent standards on nutrients. The results suggest that the procedure can support the decision-making process under uncertainty conditions and that it can enhance the likelihood of meeting effluent standards without entailing above-normal capital investments. The analysis led to reducing the capital investment by 43%, producing savings of more than 1.2 million euro.

Computer Simulation↗
PubMed1992

SCOUT: information retrieval from full-text medical literature.

SCOUT is an expert system designed to aid the practicing physician in searching a full-text database of biomedical literature for information in the domain of oncology. In the clinical setting, an information-retrieval system needs to provide only a few relevant documents that address a patient-specific query. SCOUT models the expertise of an oncologist and an information specialist in a probabilistic inference system; it applies expert heuristics both in selecting the best source of information for a particular question and in developing an effective, source-specific search statement that retrieves only a minimal number of pertinent documents.

Expert Systems↗
PubMed1997

Anesthesia patient risk: a quantitative approach to organizational factors and risk management options.

The risk of death or brain damage to anesthesia patients is relatively low, particularly for healthy patients in modern hospitals. When an accident does occur, its cause is usually an error made by the anesthesiologist, either in triggering the accident sequence, or failing to take timely corrective measures. This paper presents a pilot study which explores the feasibility of extending probabilistic risk analysis (PRA) of anesthesia accidents to assess the effects of human and management components on the patient risk. We develop first a classic PRA model for the patient risk per operation. We then link the probabilities of the different accident types to their root causes using a probabilistic analysis of the performance shaping factors. These factors are described here as the "state of the anesthesiologist" characterized both in terms of alertness and competence. We then analyze the effects of different management factors that affect the state of the anesthesiologist and we compute the risk reduction benefits of several risk management policies. Our data sources include the published version of the Australian Incident Monitoring Study as well as expert opinions. We conclude that patient risk could be reduced substantially by closer supervision of residents, the use of anesthesia simulators both in training and for periodic recertification, and regular medical examinations for all anesthesiologists.

Accidents↗
PubMed2002

Hazard and risk assessment of chemicals for terrestrial ecosystems.

Risk assessment for terrestrial ecosystems represents a great challenge due to their complexity. Pragmatic approaches, such as independent assessments for soil and 'above soil' organisms, are unrealistic. This communication presents a workable alternative, extending the role of the hazard identification. For each chemical, a set of selected ecological receptors and exposure routes is considered. Terrestrial vertebrates, soil-ground-foliar dwelling invertebrates, plants, and soil micro-organisms, are potential receptors subjected to direct and indirect exposures. Direct exposures cover those related to the emissions during the Life Cycle of the chemical. Indirect exposures focus on those occurring after the emission, related to the fate and behaviour of the molecule in the environment. Direct exposures are regulated by the production-use-disposal patterns. Indirect exposures are regulated by intrinsic (physicochemical, biological) properties. Hazard identification considers the toxicological profile and the exposure potential for each receptor, and selects the key receptors for the assessment. Risk analysis includes, for each receptor, all potential exposure routes and the corresponding timings. Food chain biomagnification is quantified on the basis of toxicokinetic data and a three vertebrate species model, suitable for top-predators and humans. A higher tier approach, considering the lack of homogeneity of the exposure and probabilistic assumptions, can also be conducted.

Animals↗