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At least 325 records · Page 18Linked to original sources
PubMed2006

Common polymorphisms in the CACNA1H gene associated with childhood absence epilepsy in Chinese Han population.

Variants with a relatively high frequency in the CACNA1H gene have previously been identified in cases of childhood absence epilepsy (CAE) in the Chinese Han population most of which are located in exons 6 to 12. In present study we attempted to further investigate whether the CACNA1H gene is associated with CAE. Exons 6 to 12 of CACNA1H gene were sequenced in samples of 100 CAE trios recruited consecutively, and 191 normal human controls. Single nucleotide polymorphisms (SNPs) were studied in both single locus and haplotype analyses in 218 CAE trios, of which 118 trios were selected from our previous research. Case-control comparisons and the transmission disequilibrium test (TDT) both supported a coding SNP (cSNP) rs9934839 (R603R) in exon 9 as being close related to CAE. The carriers of the G allele of rs9934839 had a 3-fold higher risk of CAE than non-carriers. Moreover, another cSNP rs8044363 was predicted to be connected directly with CAE in a Bayesian network. In addition, two haplotypes consisting of five cSNPs in the region of CACNA1H were statistically associated with CAE. Our research provides new evidence to further support the hypothesis that CACNA1H may be an important susceptibility gene for CAE in the Chinese Han population.

Asian People↗
PubMed2003

Risk factors for symptomatic gallstones in patients with liver cirrhosis: a case-control study.

OBJECTIVES: Liver cirrhosis is a well-documented risk factor for the formation of gallstones. In cirrhotic patients, gallstones are almost always "silent," and surgery is rarely required. When indicated (symptoms or complications), cholecystectomy implies a high morbidity risk in these patients, especially in the advanced stages of cirrhosis. The aim of this study was to estimate the risk factors for symptom development in cirrhotic patients with gallstones to identify the subgroup of patients at risk of undergoing surgery. METHODS: A total of 140 patients with liver cirrhosis and gallstones were studied: 97 with asymptomatic and 43 with symptomatic gallstone disease. The risk factors for gallstone formation (age, gender, family history, parity, obesity, diabetes mellitus, hyperlipoproteinemia) and the characteristics of liver cirrhosis (etiology, duration, Child class, hypersplenism), gallstones (duration, number, size), and gallbladder (size, wall thickness) were assessed in all patients. In 12 patients (four symptomatic, eight asymptomatic), gallbladder emptying was also evaluated by ultrasound. The association of asymptomatic and symptomatic gallstones with all these parameters was statistically evaluated by Student's t, Mann-Whitney, and chi(2) tests, as well as by means of multiple logistic regression. The causal relationship between these characteristics and gallstone symptoms was also examined by means of the KDD (knowledge discovery from databases) method, with an algorithm for learning Bayesian networks. RESULTS: Advanced age, female gender, viral etiology of cirrhosis, family history of gallstones, and duration of gallstone disease were significantly associated with symptomatic gallstone disease. The number or size of gallstones and the size or emptying of the gallbladder did not differ in symptomatic versus asymptomatic patients. Male gender and alcoholic cirrhosis were inversely correlated with symptom presence. In the multivariate analysis, family history (p = 0.0098) and advanced age (p = 0.0422) were positively correlated and male gender (p = 0.0049) and alcoholic etiology of cirrhosis (p = 0.0116) negatively correlated with symptom presence. These relationships (except for age) were also evidenced by the KDD method. CONCLUSIONS: The risk of gallstones becoming symptomatic is significantly lower in men and in alcoholic cirrhosis. In cirrhotic women, and especially in the presence of a positive family history and of advanced age, the risk of developing symptoms and undergoing surgery was significantly greater.

Adult↗
PubMed2000

Approximate maximum entropy joint feature inference consistent with arbitrary lower-order probability constraints: application to statistical classification

We propose a new learning method for discrete space statistical classifiers. Similar to Chow and Liu (1968) and Cheeseman (1983), we cast classification/inference within the more general framework of estimating the joint probability mass function (p.m.f.) for the (feature vector, class label) pair. Cheeseman's proposal to build the maximum entropy (ME) joint p.m.f. consistent with general lower-order probability constraints is in principle powerful, allowing general dependencies between features. However, enormous learning complexity has severely limited the use of this approach. Alternative models such as Bayesian networks (BNs) require explicit determination of conditional independencies. These may be difficult to assess given limited data. Here we propose an approximate ME method, which, like previous methods, incorporates general constraints while retaining quite tractable learning. The new method restricts joint p.m.f. support during learning to a small subset of the full feature space. Classification gains are realized over dependence trees, tree-augmented naive Bayes networks, BNs trained by the Kutato algorithm, and multilayer perceptrons. Extensions to more general inference problems are indicated. We also propose a novel exact inference method when there are several missing features.

Journal Article↗
PubMed2000

Correctness of local probability in graphical models with loops.

Graphical models, such as Bayesian networks and Markov networks, represent joint distributions over a set of variables by means of a graph. When the graph is singly connected, local propagation rules of the sort proposed by Pearl (1988) are guaranteed to converge to the correct posterior probabilities. Recently a number of researchers have empirically demonstrated good performance of these same local propagation schemes on graphs with loops, but a theoretical understanding of this performance has yet to be achieved. For graphical models with a single loop, we derive an analytical relationship between the probabilities computed using local propagation and the correct marginals. Using this relationship we show a category of graphical models with loops for which local propagation gives rise to provably optimal maximum a posteriori assignments (although the computed marginals will be incorrect). We also show how nodes can use local information in the messages they receive in order to correct their computed marginals. We discuss how these results can be extended to graphical models with multiple loops and show simulation results suggesting that some properties of propagation on single-loop graphs may hold for a larger class of graphs. Specifically we discuss the implication of our results for understanding a class of recently proposed error-correcting codes known as turbo codes.

Bayes Theorem↗
PubMed2001

Correctness of belief propagation in Gaussian graphical models of arbitrary topology.

Graphical models, such as Bayesian networks and Markov random fields, represent statistical dependencies of variables by a graph. Local "belief propagation" rules of the sort proposed by Pearl (1988) are guaranteed to converge to the correct posterior probabilities in singly connected graphs. Recently, good performance has been obtained by using these same rules on graphs with loops, a method we refer to as loopy belief propagation. Perhaps the most dramatic instance is the near Shannon-limit performance of "Turbo codes," whose decoding algorithm is equivalent to loopy propagation. Except for the case of graphs with a single loop, there has been little theoretical understanding of loopy propagation. Here we analyze belief propagation in networks with arbitrary topologies when the nodes in the graph describe jointly gaussian random variables. We give an analytical formula relating the true posterior probabilities with those calculated using loopy propagation. We give sufficient conditions for convergence and show that when belief propagation converges, it gives the correct posterior means for all graph topologies, not just networks with a single loop. These results motivate using the powerful belief propagation algorithm in a broader class of networks and help clarify the empirical performance results.

Journal Article↗
PubMed2005

The estimation of distributions and the minimum relative entropy principle.

Estimation of Distribution Algorithms (EDA) have been proposed as an extension of genetic algorithms. In this paper we explain the relationship of EDA to algorithms developed in statistics, artificial intelligence, and statistical physics. The major design issues are discussed within a general interdisciplinary framework. It is shown that maximum entropy approximations play a crucial role. All proposed algorithms try to minimize the Kullback-Leibler divergence KLD between the unknown distribution p(x) and a class q(x) of approximations. However, the Kullback-Leibler divergence is not symmetric. Approximations which suppose that the function to be optimized is additively decomposed (ADF) minimize KLD(q||p), the methods which learn the approximate model from data minimize KLD(p||q). This minimization is identical to maximizing the log-likelihood. In the paper three classes of algorithms are discussed. FDA uses the ADF to compute an approximate factorization of the unknown distribution. The factors are marginal distributions, whose values are computed from samples. The second class is represented by the Bethe-Kikuchi approach which has recently been rediscovered in statistical physics. Here the values of the marginals are computed from a difficult constrained minimization problem. The third class learns the factorization from the data. We analyze our learning algorithm LFDA in detail. It is shown that learning is faced with two problems: first, to detect the important dependencies between the variables, and second, to create an acyclic Bayesian network of bounded clique size.

Algorithms↗
PubMed2006

Multivariate information bottleneck.

The information bottleneck (IB) method is an unsupervised model independent data organization technique. Given a joint distribution, p(X, Y), this method constructs a new variable, T, that extracts partitions, or clusters, over the values of X that are informative about Y. Algorithms that are motivated by the IB method have already been applied to text classification, gene expression, neural code, and spectral analysis. Here, we introduce a general principled framework for multivariate extensions of the IB method. This allows us to consider multiple systems of data partitions that are interrelated. Our approach utilizes Bayesian networks for specifying the systems of clusters and which information terms should be maintained. We show that this construction provides insights about bottleneck variations and enables us to characterize the solutions of these variations. We also present four different algorithmic approaches that allow us to construct solutions in practice and apply them to several real-world problems.

Algorithms↗
PubMed2006

The cluster variation method for efficient linkage analysis on extended pedigrees.

BACKGROUND: Computing exact multipoint LOD scores for extended pedigrees rapidly becomes infeasible as the number of markers and untyped individuals increase. When markers are excluded from the computation, significant power may be lost. Therefore accurate approximate methods which take into account all markers are desirable. METHODS: We present a novel method for efficient estimation of LOD scores on extended pedigrees. Our approach is based on the Cluster Variation Method, which deterministically estimates likelihoods by performing exact computations on tractable subsets of variables (clusters) of a Bayesian network. First a distribution over inheritances on the marker loci is approximated with the Cluster Variation Method. Then this distribution is used to estimate the LOD score for each location of the trait locus. RESULTS: First we demonstrate that significant power may be lost if markers are ignored in the multi-point analysis. On a set of pedigrees where exact computation is possible we compare the estimates of the LOD scores obtained with our method to the exact LOD scores. Secondly, we compare our method to a state of the art MCMC sampler. When both methods are given equal computation time, our method is more efficient. Finally, we show that CVM scales to large problem instances. CONCLUSION: We conclude that the Cluster Variation Method is as accurate as MCMC and generally is more efficient. Our method is a promising alternative to approaches based on MCMC sampling.

Alleles↗
PubMed2004

New developments in cancer-related computational statistics.

A brief overview is presented of recently developed and currently emerging statistical and computational techniques that have been proved to be highly helpful in handling the avalanche of the new type of data generated by modern high-throughput technologies in experimental biology. The review, in no way comprehensive, focuses attention on Bayesian Networks, Hidden Markov Chain, and methods of chaotic dynamics for time-course genomic data; innovative methods in optimization and clustering; and multiple testing in the context of identification of differentially expressed genes.

Bayes Theorem↗
PubMed2005

Refining protein subcellular localization.

The study of protein subcellular localization is important to elucidate protein function. Even in well-studied organisms such as yeast, experimental methods have not been able to provide a full coverage of localization. The development of bioinformatic predictors of localization can bridge this gap. We have created a Bayesian network predictor called PSLT2 that considers diverse protein characteristics, including the combinatorial presence of InterPro motifs and protein interaction data. We compared the localization predictions of PSLT2 to high-throughput experimental localization datasets. Disagreements between these methods generally involve proteins that transit through or reside in the secretory pathway. We used our multi-compartmental predictions to refine the localization annotations of yeast proteins primarily by distinguishing between soluble lumenal proteins and soluble proteins peripherally associated with organelles. To our knowledge, this is the first tool to provide this functionality. We used these sub-compartmental predictions to characterize cellular processes on an organellar scale. The integration of diverse protein characteristics and protein interaction data in an appropriate setting can lead to high-quality detailed localization annotations for whole proteomes. This type of resource is instrumental in developing models of whole organelles that provide insight into the extent of interaction and communication between organelles and help define organellar functionality.

Amino Acid Motifs↗
PubMed2006

Support vector machine for discrimination of thermophilic and mesophilic proteins based on amino acid composition.

The identification of the thermostability from the amino acid sequence information would be helpful in computational screening for thermostable proteins. We have developed a method to discriminate thermophilic and mesophilic proteins based on support vector machines. Using self-consistency validation, 5-fold cross-validation and independent testing procedure with other datasets, this module achieved overall accuracy of 94.2%, 90.5% and 92.4%, respectively. The performance of this SVM-based module was better than the classifiers built using alternative machine learning and statistical algorithms including artificial neural networks, Bayesian statistics, and decision trees, when evaluated using these three validation methods. The influence of protein size on prediction accuracy was also addressed.

Amino Acids↗
PubMed2005

Continuous-data diagnostic tests for paratuberculosis as a multistage disease.

We devised a general method for interpretation of multistage diseases using continuous-data diagnostic tests. As an example, we used paratuberculosis as a multistage infection with 2 stages of infection as well as a noninfected state. Using data from a Danish research project, a fecal culture testing scheme was linked to an indirect ELISA and adjusted for covariates (parity, age at first calving, and days in milk). We used the log-transformed optical densities in a Bayesian network to obtain the probabilities for each of the 3 infection stages for a given optical density (adjusted for covariates). The strength of this approach was that the uncertainty associated with a test was imposed directly on the individual test result rather than aggregated into the population-based measures of test properties (i.e., sensitivity and specificity).

Aging↗
PubMed1999

A study in causal discovery from population-based infant birth and death records.

In the domain of medicine, identification of the causal factors of diseases and outcomes, helps us formulate better management, prevention and control strategies for the improvement of health care. With the goal of exploring, evaluating and refining techniques to learn causal relationships from observational data, such as data routinely collected in healthcare settings, we focused on investigating factors that may contribute causally to infant mortality in the United States. We used the U.S. Linked Birth/Infant Death dataset for 1991 with more than four million records and about 200 variables for each record. Our sample consisted of 41,155 records randomly selected from the whole dataset. Each record had maternal, paternal and child factors and the outcome at the end of the first year--whether the infant survived or not. For causal discovery we used a modified Local Causal Discovery (LCD2) algorithm, which uses the framework of causal Bayesian Networks to represent causal relationships among model variables. LCD2 takes as input a dataset and outputs causes of the form variable X causes variable Y. Using the infant birth and death dataset as input, LCD2 output nine purported causal relationships. Eight out of the nine relationships seem plausible. Even though we have not yet discovered a clinically novel causal link, we plan to look for novel causal pathways using the full sample after refining the algorithm and developing a more efficient implementation.

Algorithms↗
PubMed2000

Automatic identification of patients eligible for a pneumonia guideline.

OBJECTIVE: To assess the ability of an integrated, real-time diagnostic system (Bayesian network) to identify patients with community-acquired pneumonia who are eligible for a computerized pneumonia guideline without requiring clinicians to enter additional data. DESIGN: Prospective validation study. PATIENTS: All patients 18 years and older who presented to the emergency department of a tertiary care hospital. METHODS: The diagnostic system computed a probability of pneumonia for every patient. The final diagnosis was established using ICD-9 discharge diagnoses. Outcome measures were sensitivity, specificity, predictive values, likelihood ratios, area under the receiver operating characteristic curve, and test effectiveness. RESULTS: During the 9-week study period there were 4,361 patients (112 pneumonia patients). The area under the receiver operating characteristic curve was 0.930 (CI: 0.907, 0.948). At a fixed sensitivity of 95%, the specificity was 68.5%, the positive predictive value 7.3%, the negative predictive value 99.8%, the positive likelihood ratio 3.0, the negative likelihood ratio 0.08, and the test effectiveness 2.05. CONCLUSION: The diagnostic system was able to detect patients who are eligible for a pneumonia guideline. The detection of eligible patients can be applied to automatically initiate and evaluate computerized guidelines.

Adult↗
PubMed2000

Causal discovery from medical textual data.

Medical records usually incorporate investigative reports, historical notes, patient encounters or discharge summaries as textual data. This study focused on learning causal relationships from intensive care unit (ICU) discharge summaries of 1611 patients. Identification of the causal factors of clinical conditions and outcomes can help us formulate better management, prevention and control strategies for the improvement of health care. For causal discovery we applied the Local Causal Discovery (LCD) algorithm, which uses the framework of causal Bayesian Networks to represent causal relationships among model variables. LCD takes as input a dataset and outputs causes of the form variable Y causally influences variable Z. Using the words that occur in the discharge summaries as attributes for input, LCD output 8 purported causal relationships. The relationships ranked as most probable subjectively appear to be most causally plausible.

Algorithms↗
PubMed2001

Automatic identification of patients eligible for a pneumonia guideline: comparing the diagnostic accuracy of two decision support models.

BACKGROUND: In busy clinical settings, physicians often do not have enough time to identify patients for specific therapeutic guidelines. As a solution, decision support systems could automatically identify eligible patients and trigger computerized guidelines for specific diseases. Applying this idea to community-acquired pneumonia (CAP), we developed a Bayesian network (BN) and an artificial neural network (ANN) for identifying patients who have CAP and are eligible for a pneumonia guideline. OBJECTIVE: The aim of this study was to determine whether the diagnostic accuracy of these two decision support models differs in terms of identifying CAP patients. METHODS: We trained and tested the networks with a data set of 32,662 adult patients. For each network, we (1) calculated the specificity, the positive predictive value (PPV), and the negative predictive value (NPV) at a sensitivity of 95%, and (2) determined the area under the receiver operating characteristic curve (AUC) as a measure of overall accuracy. We tested for statistical difference between the AUCs using the correlated area z statistic. RESULTS: At a sensitivity of 95%, the respective values for specificity, PPV, and NPV were: 92.3%, 15.1%, and 99.9% for the BN, and 94.0%, 18.6%, and 99.9% for the ANN. The BN had an AUC of 0.9795 (95% CI: 0.9736, 0.9843), and the ANN had an AUC of 0.9855 (95% CI: 0.9805, 0.9894). The difference between the AUCs was statistically significant (p=0.0044). CONCLUSIONS: The networks achieved high overall accuracies on the testing data set. Because the difference in accuracies is statistically significant but not clinically significant, both networks are equally suited to drive a guideline.

Area Under Curve↗
PubMed2001

Finding appropriate clinical trials: evaluating encoded eligibility criteria with incomplete data.

We describe our work on creating a system that selects appropriate clinical trials by automating the evaluation of eligibility criteria. We developed a data model of eligibility for breast cancer clinical trials, upon which the criteria were encoded. Standard vocabularies are utilized to represent concepts used in the system, and retrieve their hierarchical relationships. The system incorporates Bayesian networks to handle missing patient information. Protocols are ranked by the belief that the patient is eligible for each of them. In a preliminary evaluation, we found good agreement (kappa 0.86) between the system and an independent physician in selection of protocols, but poor agreement (kappa 0.24) in protocol ranking. We conclude that our approach is feasible, and potentially useful in assisting both physicians and patients in the task of selecting appropriate trials.

Bayes Theorem↗
PubMed2003

A computer-based microarray experiment design-system for gene-regulation pathway discovery.

This paper reports the methods and evaluation of a computer-based system that recommends microarray experimental design for biologists - causal discovery in Gene Expression data using Expected Value of Experimentation (GEEVE). The GEEVE system uses causal Bayesian networks and generates a decision tree for recommendations. To evaluate the GEEVE system, we first built an expression simulation model based on a gene regulation model assessed by an expert biologist. Using the simulation model, we conducted a controlled study that involved 10 biologists, some of whom used GEEVE and some of whom did not. The results show that biologists who used GEEVE reached correct causal assessments about gene regulation more often than did those biologists who did not use GEEVE.

Area Under Curve↗