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PubMed2005

A probabilistic approach to the joint evaluation of firearm evidence and gunshot residues.

The present paper addresses issues that affect both the separate as well as the joint evaluation of firearm evidence (i.e., marks) and gunshot residues (GSR). Mark evidence will be used as a basis to discriminate among barrels through which a bullet in question might have been shot whereas GSR will be used to draw inferences about the distance of firing. Particular attention is drawn to the coherent handling of uncertainties associated with the various parameters considered within each item of evidence. The proposed analysis relies on a probabilistic viewpoint that uses graphical models (i.e., Bayesian networks) as an aid to cope with the complexity induced by the number of variables considered. The paper discusses how an approach based on a probabilistic network environment can be used for the formal analysis and construction of arguments. Emphasis is made on the gain of insight into structural dependencies that may be uncovered when the evaluative process is extended beyond single items of scientific evidence.

Bayes Theorem↗
PubMed2006

A field model for human detection and tracking.

The large shape variability and partial occlusions challenge most object detection and tracking methods for nonrigid targets such as pedestrians. This paper presents a new approach based on a two-layer statistical field model that characterizes the prior of the complex shape variations as a Boltzmann distribution and embeds this prior and the complex image likelihood into a Markov field. A probabilistic variational analysis of this model reveals a set of fixed-point equations characterizing the equilibrium of the field. It leads to computationally efficient methods for calculating the image likelihood and for training the model. Based on that, effective algorithms for detecting nonrigid objects are developed. This new approach has several advantages. First, it is intrinsically suitable for capturing local nonrigidity. In addition, due to the distributed likelihood, this approach is robust to partial occlusions. Moreover, the two-layer structure provides large flexibility of modeling the image observations, which makes the new method robust to clutters. Extensive experiments demonstrate its effectiveness.

Algorithms↗
PubMed2001

SAMIE: statistical algorithm for modeling interaction energies.

We are investigating the rules that govern protein-DNA interactions, using a statistical mechanics based formalism that is related to the Boltzmann Machine of the neural net literature. Our approach is data-driven, in which probabilistic algorithms are used to model protein-DNA interactions, given SELEX and/or phage data as input. In the current report, we trained the network using SELEX data, under the "one-to-one" model of interactions (i.e. one amino acid contacts one base). The trained network was able to successfully identify the wild-type binding sites of EGR and MIG protein families. The predictions using our method are the same or better than that of methods existing in the literature. However our methodology offers the potential to capitalise in quantitative detail, as well as to be used to explore more general model of interactions, given availability of data.

Algorithms↗
PubMed2003

Research in medical problem solving: a review.

This article is a review of problem-solving models that have been studied in the medical domain. These models seek to describe and evaluate the cognitive processes activated in problem solving in the medical domain and to use this knowledge to further evaluate expert-novice differences. The principle models discussed are the hypothetico-deductive, illness scripts, and the probabilistic. After reviewing these problem-solving models, the reasoning strategies identified in medical problem solving will be discussed. These strategies, forward-backward and broad-narrow, are analyzed as indicators of expert versus novice problem solving in the medical domain. Finally, the methods identified for studying medical problem solving-simulated patients, stimulated recall, think-aloud protocol, and the Pathfinder algorithm-will be discussed.

Animals↗
PubMed2002

Predicting the frequency of water quality standard violations: a probabilistic approach for TMDL development.

To address the impaired condition of the water bodies listed under Section 303(d) of the Clean Water Act, over 40 000 total maximum daily loads (TMDLs) for pollutants must be developed during the next 10-15 years. Most of these will be based on the results of water quality simulation models. However, the failure of most models to incorporate residual variability and parameter uncertainty in their predictions makes them unsuitable for TMDL development. The percentile-based standards increasingly used by the EPA and the requirement for a margin of safety in TMDLs necessitate that model predictions include quantitative information on uncertainty. We describe a probabilistic approach to model-based TMDL assessment that addresses this issue and is suitable for use with any type of mathematical model. To demonstrate our approach, we employ a eutrophication model for the Neuse River Estuary, North Carolina, and evaluate compliance with the state chlorophyll a standard. Any observed variability in chlorophyll athatis notexplained bythe model is explicitly incorporated via a residual error term. This probabilistic term captures the effects of any processes that are not considered in the model and allows for direct assessment of the frequency of standard violations. Additionally, by estimating and propagating the effects of parameter uncertainty on model predictions, we are able to provide an explicit basis for choosing a TMDL that includes a margin of safety. We conclude by discussing the potential for models currently supported by the EPA to be adapted to provide the type of probabilistic information that is necessary to support TMDL decisions.

Decision Making↗
PubMed2000

Modeling synaptic plasticity in conjuction with the timing of pre- and postsynaptic action potentials.

We present a spiking neuron model that allows for an analytic calculation of the correlations between pre- and postsynaptic spikes. The neuron model is a generalization of the integrate-and-fire model and equipped with a probabilistic spike-triggering mechanism. We show that under certain biologically plausible conditions, pre- and postsynaptic spike trains can be described simultaneously as an inhomogeneous Poisson process. Inspired by experimental findings, we develop a model for synaptic long-term plasticity that relies on the relative timing of pre- and post-synaptic action potentials. Being given an input statistics, we compute the stationary synaptic weights that result from the temporal correlations between the pre- and postsynaptic spikes. By means of both analytic calculations and computer simulations, we show that such a mechanism of synaptic plasticity is able to strengthen those input synapses that convey precisely timed spikes at the expense of synapses that deliver spikes with a broad temporal distribution. This may be of vital importance for any kind of information processing based on spiking neurons and temporal coding.

Action Potentials↗
PubMed2006

A hidden Markov model approach for determining expression from genomic tiling micro arrays.

BACKGROUND: Genomic tiling micro arrays have great potential for identifying previously undiscovered coding as well as non-coding transcription. To-date, however, analyses of these data have been performed in an ad hoc fashion. RESULTS: We present a probabilistic procedure, ExpressHMM, that adaptively models tiling data prior to predicting expression on genomic sequence. A hidden Markov model (HMM) is used to model the distributions of tiling array probe scores in expressed and non-expressed regions. The HMM is trained on sets of probes mapped to regions of annotated expression and non-expression. Subsequently, prediction of transcribed fragments is made on tiled genomic sequence. The prediction is accompanied by an expression probability curve for visual inspection of the supporting evidence. We test ExpressHMM on data from the Cheng et al. (2005) tiling array experiments on ten Human chromosomes. Results can be downloaded and viewed from our web site. CONCLUSION: The value of adaptive modelling of fluorescence scores prior to categorisation into expressed and non-expressed probes is demonstrated. Our results indicate that our adaptive approach is superior to the previous analysis in terms of nucleotide sensitivity and transfrag specificity.

Algorithms↗
PubMedDate not supplied

Use of influence diagrams to structure medical decisions.

Influence diagrams are compact representations of decision problems that are mathematically equivalent to decision trees. The authors present five important principles for structuring a decision as an influence diagram: 1) start at the value node and work back to the decision nodes; 2) draw the arcs in the direction that makes the probabilities easiest to assess; 3) use informational arcs to specify which events will have been observed at the time each decision is made; 4) ensure that missing arcs reflect intentional assertions about conditional independence and the timing of observations; and 5) ensure that there are no cycles in the influence diagram. They then build an influence diagram for the problem of staging non-small-cell lung cancer as an illustration. Influence diagrams offer several strengths for structuring medical decisions. They represent graphically and compactly the probabilistic relationships between parameters in the model. Influence diagrams also allow the model to be structured in a fashion that eases the necessary probability assessments, regardless of whether the assessments are based on available evidence or on expert judgment. Influence diagrams provide an important complement to decision trees, especially for representing probabilistic relationships among variables in a decision model.

Carcinoma, Non-Small-Cell Lung↗
PubMed1996

A probabilistic classification system for predicting the cellular localization sites of proteins.

We have defined a simple model of classification which combines human provided expert knowledge with probabilistic reasoning. We have developed software to implement this model and have applied it to the problem of classifying proteins into their various cellular localization sites based on their amino acid sequences. Since our system requires no hand tuning to learn training data, we can now evaluate the prediction accuracy of protein localization sites by a more objective cross-validation method than earlier studies using production rule type expert systems. 336 E. coli proteins were classified into 8 classes with an accuracy of 81% while 1484 yeast proteins were classified into 10 classes with an accuracy of 55%. Additionally we report empirical results using three different strategies for handling continuously valued variables in our probabilistic reasoning system.

Cells↗
PubMed2005

Accounting for probe-level noise in principal component analysis of microarray data.

MOTIVATION: Principal Component Analysis (PCA) is one of the most popular dimensionality reduction techniques for the analysis of high-dimensional datasets. However, in its standard form, it does not take into account any error measures associated with the data points beyond a standard spherical noise. This indiscriminate nature provides one of its main weaknesses when applied to biological data with inherently large variability, such as expression levels measured with microarrays. Methods now exist for extracting credibility intervals from the probe-level analysis of cDNA and oligonucleotide microarray experiments. These credibility intervals are gene and experiment specific, and can be propagated through an appropriate probabilistic downstream analysis. RESULTS: We propose a new model-based approach to PCA that takes into account the variances associated with each gene in each experiment. We develop an efficient EM-algorithm to estimate the parameters of our new model. The model provides significantly better results than standard PCA, while remaining computationally reasonable. We show how the model can be used to 'denoise' a microarray dataset leading to improved expression profiles and tighter clustering across profiles. The probabilistic nature of the model means that the correct number of principal components is automatically obtained.

Algorithms↗
PubMed2006

Discovering spatio-temporal models of the spread of West Nile virus.

Emerging infectious diseases are characterized by complex interactions among disease agents, vectors, wildlife, humans, and the environment. Since the appearance of West Nile virus (WNV) in New York City in 1999, it has infected over 8,000 people in the United States, resulting in several hundred deaths in 46 contiguous states. The virus is transmitted by mosquitoes and maintained in various bird reservoir hosts. Its unexpected introduction, high morbidity, and rapid spread have left public health agencies facing severe time constraints in a theory-poor environment, dependent largely on observational data collected by independent survey efforts and much uncertainty. Current knowledge may be expressed as a priori constraints on models learned from data. Accordingly, we applied a Bayesian probabilistic relational approach to generate spatially and temporally linked models from heterogeneous data sources. Using data collected from multiple independent sources in Maryland, we discovered the integrated context in which infected birds are plausible indicators for positive mosquito pools and human cases for 2001 and 2002.

Animals↗
PubMed2006

Cost utility analysis of physical activity counselling in general practice.

OBJECTIVE: To evaluate the economic performance of the 'Green Prescription' physical activity counselling program in general practice. METHODS: Cost utility analysis using a Markov model was used to estimate the cost utility of the Green Prescription program over full life expectancy. Program effectiveness was based on published trial data (878 inactive patients presenting to NZ general practice). Costs were based on detailed costing information and were discounted at 5% per anum. The main outcome measure is cost per quality adjusted life year (QALY) gained. Extensive one-way sensitivity analyses were performed along with probabilistic (stochastic) analysis. RESULTS: Incremental, modelled cost utility of the Green Prescription program compared with 'usual care' was dollar NZ2,053 per QALY gained over full life expectancy (range dollar NZ827 to dollar NZ37,516 per QALY). Based on the probabilistic sensitivity analysis, 90% of ICERs fell below dollar NZ7,500 per QALY. CONCLUSIONS: Based on a plausible and conservative set of assumptions, if decision makers are willing to pay at least dollar NZ2,000 per QALY gained the Green Prescription program is likely to represent better value for money than 'usual care'. IMPLICATIONS: The Green Prescription program performs well, representing a good buy relative to other published cost effectiveness estimates. Policy makers should consider encouraging general practitioners to prescribe physical activity advice in the primary care setting, in association with support from exercise specialists.

Adult↗
PubMed2004

CIS: compound importance sampling method for protein-DNA binding site p-value estimation.

MOTIVATION: A key aspect of transcriptional regulation is the binding of transcription factors to sequence-specific binding sites that allow them to modulate the expression of nearby genes. Given models of such binding sites, one can scan regulatory regions for putative binding sites and construct a genome-wide regulatory network. In such genome-wide scans, it is crucial to control the amount of false positive predictions. Recently, several works demonstrated the benefits of modeling dependencies between positions within the binding site. Yet, computing the statistical significance of putative binding sites in this scenario remains a challenge. RESULTS: We present a general, accurate and efficient method for computing p-values of putative binding sites that is applicable to a large class of probabilistic binding site and background models. We demonstrate the accuracy of the method on synthetic and real-life data. AVAILABILITY: The procedure for scanning DNA sequences and computing the statistical significance of putative binding site scores is available upon request at http://compbio.cs.huji.ac.il/CIS/ CONTACT: nir@cs.huji.ac.il.

Algorithms↗
PubMed2005

A new image representation algorithm inspired by image submodality models, redundancy reduction, and learning in biological vision.

We develop a new biologically motivated algorithm for representing natural images using successive projections into complementary subspaces. An image is first projected into an edge subspace spanned using an ICA basis adapted to natural images which captures the sharp features of an image like edges and curves. The residual image obtained after extraction of the sharp image features is approximated using a mixture of probabilistic principal component analyzers (MPPCA) model. The model is consistent with cellular, functional, information theoretic, and learning paradigms in visual pathway modeling. We demonstrate the efficiency of our model for representing different attributes of natural images like color and luminance. We compare the performance of our model in terms of quality of representation against commonly used basis, like the discrete cosine transform (DCT), independent component analysis (ICA), and principal components analysis (PCA), based on their entropies. Chrominance and luminance components of images are represented using codes having lower entropy than DCT, ICA, or PCA for similar visual quality. The model attains considerable simplification for learning from images by using a sparse independent code for representing edges and explicitly evaluating probabilities in the residual subspace.

Algorithms↗
PubMed2004

Segmentation of 4D cardiac MR images using a probabilistic atlas and the EM algorithm.

In this paper an automatic atlas-based segmentation algorithm for 4D cardiac MR images is proposed. The algorithm is based on the 4D extension of the expectation maximisation (EM) algorithm. The EM algorithm uses a 4D probabilistic cardiac atlas to estimate the initial model parameters and to integrate a priori information into the classification process. The probabilistic cardiac atlas has been constructed from the manual segmentations of 3D cardiac image sequences of 14 healthy volunteers. It provides space and time-varying probability maps for the left and right ventricles, the myocardium, and background structures such as the liver, stomach, lungs and skin. In addition to using the probabilistic cardiac atlas as a priori information, the segmentation algorithm incorporates spatial and temporal contextual information by using 4D Markov Random Fields. After the classification, the largest connected component of each structure is extracted using a global connectivity filter which improves the results significantly, especially for the myocardium. Validation against manual segmentations and computation of the correlation between manual and automatic segmentation on 249 3D volumes were calculated. We used the 'leave one out' test where the image set to be segmented was not used in the construction of its corresponding atlas. Results show that the procedure can successfully segment the left ventricle (LV) (r = 0.96), myocardium (r = 0.92) and right ventricle (r = 0.92). In addition, 4D images from 10 patients with hypertrophic cardiomyopathy were also manually and automatically segmented yielding a good correlation in the volumes of the LV (r = 0.93) and myocardium (0.94) when the atlas constructed with volunteers is blurred.

Algorithms↗
PubMed1977

Storing covariance with nonlinearly interacting neurons.

A time-dependent, nonlinear model of neuronal interaction which was probabilistically analyzed in a previous article is shown here to be a natural generalization of the Hartline-Ratliff model of the Limulus retina. Although the primary physical variables in the model are the membrane potentials of neurons, the equations which govern the means and covariances of the membrane potentials are coupled through the average firing rates; as a consequence, the average firing rates control the selective storage and retrieval of covariance information. Motor learning in the cerebellar cortex is treated as a problem of covariance storage, and a predicition is made for the underlying synaptic plasticity: the change in synaptic strength between a parallel fiber and a Purkinje cell should be proportional to the covariance between discharges in the parallel fiber and the climbing fiber. Unlike previous proposals for synaptic plasticity, this prediction requires both facilitation and depression to occur (under different conditions) at the same synapse.

Cerebellum↗
PubMed1980

Tension fluctuations in contracting myofibrils and their interpretation.

A self-consistent cycling steady-state model of contracting muscle is postulated to relate the autocorrelation functions of force fluctuations to the kinetic constants governing the operation of the cross-bridges. The fluctuations in the concentration of various intermediates in the model are due to the probabilistic nature of the transitions between states. It is shown that the decay rate of the autocorrelation of fluctuations in force is dependent on, and only on, the two rate constants governing transitions between attached states, and hence that the experimental autocorrelation functions can be used to estimate these rate constants. The model relates the time behavior of fluctuations in the concentration of any pair of enzymatic intermediates through the cross-correlation functions of fluctuations, and thus suggests a way to establish experimentally whether coupling exists between enzymatic and mechanical events during muscle contraction.

Actomyosin↗
PubMed2004

Analysis of the threshold liability model provides new understanding of causation in autoimmune diseases.

Autoimmune diseases include a heterogeneous group of complex traits, the causes of which are essentially unknown. The threshold liability model is a hypothesis that has a significant influence on thinking about causation in these diseases. Here, I analyze this model and assess its utility in understanding causation in autoimmunity. According to the model, members of a population have a normal distribution of genetic liability for a particular autoimmune disease. Further, a threshold value exists for each autoimmune disease such that an individual develops disease when his/her liability exceeds the threshold value; environmental and stochastic factors and epistatic gene interactions may increase or decrease an individual's disease liability. There are, however, two main problems with the threshold liability model. First, for a particular autoimmune disease, the threshold value divides a population into two distinct groups that consist either of affected or of healthy individuals. I show that this dichotomous division is inaccurate and misleading. Second, the threshold value corresponds to the occurrence of a component-cause of disease, i.e. when an appropriate collection of causative factors for a particular autoimmune disease is present, the disease must inevitably occur. I argue, however, that the disease contribution of essentially unknown random or stochastic factors to causation is at least similar in importance to the contributions of genetic and environmental factors. These stochastic factors add a significant element of unpredictability to the effects of genetic and environmental factors. Consequently causes in autoimmunity do not act deterministically, which is implied by the component-cause concept. Instead, the role of causative factors is to alter disease risk. I therefore reject the threshold liability model and conclude that a probabilistic approach provides the only reasonable way to understand causation in autoimmune diseases. This conclusion has important implications for other deterministic hypotheses in autoimmunity including other component-cause hypotheses.

Autoimmune Diseases↗