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PubMed2005

Bayesian neural networks for bivariate binary data: an application to prostate cancer study.

Prostate cancer is one of the most common cancers in American men. The cancer could either be locally confined, or it could spread outside the organ. When locally confined, there are several options for treating and curing this disease. Otherwise, surgery is the only option, and in extreme cases of outside spread, it could very easily recur within a short time even after surgery and subsequent radiation therapy. Hence, it is important to know, based on pre-surgery biopsy results how likely the cancer is organ-confined or not. The paper considers a hierarchical Bayesian neural network approach for posterior prediction probabilities of certain features indicative of non-organ confined prostate cancer. In particular, we find such probabilities for margin positivity (MP) and seminal vesicle (SV) positivity jointly. The available training set consists of bivariate binary outcomes indicating the presence or absence of the two. In addition, we have certain covariates such as prostate specific antigen (PSA), gleason score and the indicator for the cancer to be unilateral or bilateral (i.e. spread on one or both sides) in one data set and gene expression microarrays in another data set. We take a hierarchical Bayesian neural network approach to find the posterior prediction probabilities for a test and validation set, and compare these with the actual outcomes for the first data set. In case of the microarray data we use leave one out cross-validation to access the accuracy of our method. We also demonstrate the superiority of our method to the other competing methods through a simulation study. The Bayesian procedure is implemented by an application of the Markov chain Monte Carlo numerical integration technique. For the problem at hand, our Bayesian bivariate neural network procedure is shown to be superior to the classical neural network, Radford Neal's Bayesian neural network as well as bivariate logistic models to predict jointly the MP and SV in a patient in both the data sets as well as in the simulation study.

Bayes Theorem↗
PubMed2005

Estimation of individual admixture: analytical and study design considerations.

The genome of an admixed individual represents a mixture of alleles from different ancestries. In the United States, the two largest minority groups, African-Americans and Hispanics, are both admixed. An understanding of the admixture proportion at an individual level (individual admixture, or IA) is valuable for both population geneticists and epidemiologists who conduct case-control association studies in these groups. Here we present an extension of a previously described frequentist (maximum likelihood or ML) approach to estimate individual admixture that allows for uncertainty in ancestral allele frequencies. We compare this approach both to prior partial likelihood based methods as well as more recently described Bayesian MCMC methods. Our full ML method demonstrates increased robustness when compared to an existing partial ML approach. Simulations also suggest that this frequentist estimator achieves similar efficiency, measured by the mean squared error criterion, as Bayesian methods but requires just a fraction of the computational time to produce point estimates, allowing for extensive analysis (e.g., simulations) not possible by Bayesian methods. Our simulation results demonstrate that inclusion of ancestral populations or their surrogates in the analysis is required by any method of IA estimation to obtain reasonable results.

Black or African American↗
PubMed2006

Vision as Bayesian inference: analysis by synthesis?

We argue that the study of human vision should be aimed at determining how humans perform natural tasks with natural images. Attempts to understand the phenomenology of vision from artificial stimuli, although worthwhile as a starting point, can lead to faulty generalizations about visual systems, because of the enormous complexity of natural images. Dealing with this complexity is daunting, but Bayesian inference on structured probability distributions offers the ability to design theories of vision that can deal with the complexity of natural images, and that use 'analysis by synthesis' strategies with intriguing similarities to the brain. We examine these strategies using recent examples from computer vision, and outline some important implications for cognitive science.

Algorithms↗
PubMed1998

Dynamic decision analysis in medicine: a data-driven approach.

Dynamic decision analysis concerns decision problems in which both time and uncertainty are explicitly considered. Two major challenges in dynamic decision analysis are on proper formulation of a model for the problem and effective elicitation of the numerous time-dependent conditional probabilities for the model. Based on a new, general dynamic decision modeling framework called DynaMoL (Dynamic decision Modeling Language), we propose a data-driven approach to addressing these issues. Our approach uses available problem data from large medical databases, guides the decision modeling at a proper level of abstraction and establishes a Bayesian learning method for automatic extraction of the probabilistic parameters. We demonstrate the theoretical implications and practical promises of this new approach to dynamic decision analysis in medicine through a comprehensive case study in the optimal follow-up of patients after curative colorectal cancer surgery.

Bayes Theorem↗
PubMed2006

Sparse Bayesian kernel survival analysis for modeling the growth domain of microbial pathogens.

Survival analysis is a branch of statistics concerned with the time elapsing before "failure," with diverse applications in medical statistics and the analysis of the reliability of electrical or mechanical components. We introduce a parametric accelerated life survival analysis model based on kernel learning methods that, at least in principal, is able to learn arbitrary dependencies between a vector of explanatory variables and the scale of the distribution of survival times. The proposed kernel survival analysis method is then used to model the growth domain of Clostridium botulinum, the food processing and storage conditions permitting the growth of this foodborne microbial pathogen, leading to the production of the neurotoxin responsible for botulism. A Bayesian training procedure, based on the evidence framework, is used for model selection and to provide a credible interval on model predictions. The kernel survival analysis models are found to be more accurate than models based on more traditional survival analysis techniques but also suggest a risk assessment of the foodborne botulism hazard would benefit from the collection of additional data.

Artificial Intelligence↗
PubMed2006

Bayesian feature and model selection for Gaussian mixture models.

We present a Bayesian method for mixture model training that simultaneously treats the feature selection and the model selection problem. The method is based on the integration of a mixture model formulation that takes into account the saliency of the features and a Bayesian approach to mixture learning that can be used to estimate the number of mixture components. The proposed learning algorithm follows the variational framework and can simultaneously optimize over the number of components, the saliency of the features, and the parameters of the mixture model. Experimental results using high-dimensional artificial and real data illustrate the effectiveness of the method.

Algorithms↗
PubMed2002

A Bayesian attractor network with incremental learning.

A realtime online learning system with capacity limits needs to gradually forget old information in order to avoid catastrophic forgetting. This can be achieved by allowing new information to overwrite old, as in a so-called palimpsest memory. This paper describes an incremental learning rule based on the Bayesian confidence propagation neural network that has palimpsest properties when employed in an attractor neural network. The network does not suffer from catastrophic forgetting, has a capacity dependent on the learning time constant and exhibits faster convergence for newer patterns.

Bayes Theorem↗
PubMed2004

Evaluation of HIV-1 kinetic models using quantitative discrimination analysis.

MOTIVATION: Since the identification of human immunodeficiency virus (HIV) over twenty years ago, many mathematical models of HIV dynamics have been proposed. The purpose of this study was to evaluate intracellular and intercellular scale HIV models that best described the dynamics of viral and cell titers of a person, where parameters were determined using typically available patient data. In this case, 'best' was defined as the model most capable of describing experimental patient data and was determined by Bayesian-based model discrimination analysis and the ability to provide realistic results. RESULTS: Twenty models of HIV-1 viral dynamics were initially evaluated to determine whether parameters could be obtained from readily available clinical data from established HIV-1 patients with stable disease. Based on this analysis, three models were chosen for further examination and comparison. Parameters were estimated using experimental data from a cohort of 338 people monitored for up to 2484 days. The models were evaluated using a Bayesian technique to determine which model was most probable. The model ultimately selected as most probable was overwhelmingly favored relative to the remaining two models, and it accounted for uninfected cells, infected cells and cytotoxic T lymphocyte dynamics. The authors developed a fourth model for comparison purposes by combining the features of the original three models. Parameters were estimated for the new model and the statistical analysis was repeated for all four models. The model that was initially favored was selected again upon model discrimination analysis. CONTACT: srivasta@engr.uconn.edu.

Cohort Studies↗
PubMed2003

Using hierarchical models to analyse clinical indicators: a comparison of the gamma-Poisson and beta-binomial models.

BACKGROUND: Clinical indicators (CIs) are used to assess, compare and determine the potential to improve the care provided by hospitals and physicians. The results for Australian hospitals in 1998-2000 have been reported using a new methodology. The gamma-Poisson hierarchical model was used to correct for the effects of sampling variation by obtaining the empirical Bayesian shrunken estimates for the CI proportions for each hospital. Then, an estimate of the potential system gains that could be achieved if the mean proportion was shifted to the 20th centile is obtained for each of the 185 CIs. The results are sed to prioritize quality improvement activity. OBJECTIVES: To describe the 20th centile method of calculating potential system gains in the health care system; to determine the impact of using the beta-binomial model rather than the gamma-Poisson model to obtain shrunken estimates for the CI proportions; and to compare the computationally simpler Method of Moments (MoM) with the maximum likelihood (ML) method for parameter estimation. METHODS: The formulae for the gamma-Poisson and beta-binomial shrinkage estimators were compared analytically. Each of the shrinkage estimators and the two methods of parameter estimation were applied to the Obstetric and Gynecological CIs, and the results compared. RESULTS The comparison of the formulae for the two shrinkage estimators showed that the gamma-Poisson model results in: greater shrinkage towards the overall mean. This was verified empirically using the clinical indicators. Additionally, the MoM was not a viable alternative to the ML method. CONCLUSIONS: The gamma-Poisson model provided smaller estimates of the potential system gains by up to 6.7% of the numerator for the clinical indicators. The difference in estimation increased with increasing mean proportions and between-hospital variation. We recommend that the beta-binomial model should be used on the basis of both theoretical and empirical grounds.

Australia↗
PubMed2004

Analyzing cellular biochemistry in terms of molecular networks.

One way to understand cells and circumscribe the function of proteins is through molecular networks. These networks take a variety of forms including webs of protein-protein interactions, regulatory circuits linking transcription factors and targets, and complex pathways of metabolic reactions. We first survey experimental techniques for mapping networks (e.g., the yeast two-hybrid screens). We then turn our attention to computational approaches for predicting networks from individual protein features, such as correlating gene expression levels or analyzing sequence coevolution. All the experimental techniques and individual predictions suffer from noise and systematic biases. These problems can be overcome to some degree through statistical integration of different experimental datasets and predictive features (e.g., within a Bayesian formalism). Next, we discuss approaches for characterizing the topology of networks, such as finding hubs and analyzing subnetworks in terms of common motifs. Finally, we close with perspectives on how network analysis represents a preliminary step toward a systems approach for modeling cells.

Biochemical Phenomena↗
PubMed2006

Clustering microarray gene expression data using weighted Chinese restaurant process.

MOTIVATION: Clustering microarray gene expression data is a powerful tool for elucidating co-regulatory relationships among genes. Many different clustering techniques have been successfully applied and the results are promising. However, substantial fluctuation contained in microarray data, lack of knowledge on the number of clusters and complex regulatory mechanisms underlying biological systems make the clustering problems tremendously challenging. RESULTS: We devised an improved model-based Bayesian approach to cluster microarray gene expression data. Cluster assignment is carried out by an iterative weighted Chinese restaurant seating scheme such that the optimal number of clusters can be determined simultaneously with cluster assignment. The predictive updating technique was applied to improve the efficiency of the Gibbs sampler. An additional step is added during reassignment to allow genes that display complex correlation relationships such as time-shifted and/or inverted to be clustered together. Analysis done on a real dataset showed that as much as 30% of significant genes clustered in the same group display complex relationships with the consensus pattern of the cluster. Other notable features including automatic handling of missing data, quantitative measures of cluster strength and assignment confidence. Synthetic and real microarray gene expression datasets were analyzed to demonstrate its performance. AVAILABILITY: A computer program named Chinese restaurant cluster (CRC) has been developed based on this algorithm. The program can be downloaded at http://www.sph.umich.edu/csg/qin/CRC/.

Algorithms↗
PubMed1999

Circadian rhythm of 5-fluorouracil population pharmacokinetics in patients with metastatic colorectal cancer.

PURPOSE: The purpose of this work was to estimate the population pharmacokinetic parameters of 5-fluorouracil (5-FU) in patients with advanced colorectal cancer using circadian change kinetics. METHODS: Eighty-five patients (32 females, 53 males) were enrolled onto this study. All patients received folinic acid (200 mg/m(2)) by intravenous infusion over 2 h followed by a 5-FU loading dose (400 mg/m(2)) and then continuous infusion (600 mg/m(2)) for 22 h. This whole regimen was repeated on day 2 and was given on a 14-day cycle. Plasma 5-FU determinations were performed by high-performance liquid chromatography with ultraviolet absorbance detection. Pharmacokinetic analyses were performed using the NONMEM computer program through the Visual-NM graphical interface. An open one-compartment pharmacokinetic model with zero-order input rate was used to describe the kinetics of 5-FU; moreover, circadian time-dependent changes in 5-FU concentrations were taken into account in the model. The circadian model was defined as the sum of two cyclic components; the amplitude of the first cyclic component (over 24 h) was about 30% of the average clearance and the amplitude of the second cyclic component (over 12 h) was about 50% of the amplitude of the first component. The acrophase (peak) times of the first and the second periodic component were 04 h 12 m and 00 h 25 m, respectively. The potential sources of variability on the population parameters (65 patients) were investigated using patient's sex, body area, age, body weight, height, liver enzymes and serum creatinine as covariables. RESULTS: Only the estimated clearance circadian changes were different for the two sexes. The population parameter estimates of mean clearance (CL(mean)) and initial volume of distribution (V), were as follows: the male subgroup showed a CL(mean) value twice larger (125 l/h) than the value observed in the female subgroup (65 l/h), and V = 21 l. A validation group of 20 additional patients was used to evaluate the predictive performances of the population parameters. The individual pharmacokinetic parameters were computed by means of a Bayesian fitting procedure. From the resulting individualized parameter values, concentrations of 5-FU in the plasma were calculated. To evaluate the performance of the Bayesian estimation, the experimental concentrations were compared with the predicted ones. CONCLUSION: In conclusion, a chronomodulated delivery schedule of 5-FU should be performed, using a perfusion rate inversely proportional to the circadian variations of clearance in order to maintain stable 5-FU plasma levels. Such a treatment schedule may result in increased effectiveness of the treatment and decreased occurrence of drug-associated side-effects. The present study develops a complete procedure to efficiently estimate 5-FU clearance in order to optimize dosage regimens in individual patients.

Aged↗
PubMed2004

Wurst: a protein threading server with a structural scoring function, sequence profiles and optimized substitution matrices.

Wurst is a protein threading program with an emphasis on high quality sequence to structure alignments (http://www.zbh.uni-hamburg.de/wurst). Submitted sequences are aligned to each of about 3000 templates with a conventional dynamic programming algorithm, but using a score function with sophisticated structure and sequence terms. The structure terms are a log-odds probability of sequence to structure fragment compatibility, obtained from a Bayesian classification procedure. A simplex optimization was used to optimize the sequence-based terms for the goal of alignment and model quality and to balance the sequence and structural contributions against each other. Both sequence and structural terms operate with sequence profiles.

Amino Acid Substitution↗
PubMed1993

Multiply sectioned Bayesian networks for neuromuscular diagnosis.

A prototype neuromuscular diagnostic system (PAINULIM) that diagnoses painful or impaired upper limbs has been developed based on Bayesian networks. This paper presents nonmathematically the major knowledge representation issues that arose in the development of PAINULIM. Motivated by the computational overhead of large application domains, and the desire to provide a user with an interface that gives a focused display of a subdomain of current interest, we built PAINULIM using the idea of multiply sectioned Bayesian networks. A preliminary evaluation of PAINULIM with 76 patients has demonstrated good clinical performance.

Artificial Intelligence↗
PubMedDate not supplied

Evaluation of binary QSAR models derived from LUDI and MOE scoring functions for structure based virtual screening.

In today's world of high-throughput in silico screening, the development of virtual screening methodologies to prioritize small molecules as new chemical entities (NCEs) for synthesis is of current interest. Among several approaches to virtual screening, structure-based virtual screening has been considered the most effective. However the problems associated with the ranking of potential solutions in terms of scoring functions remains one of the major bottlenecks in structure-based virtual screening technology. It has been suggested that scoring functions may be used as filters for distinguishing binders from nonbinders instead of accurately predicting their binding free energies. Subsequently, several improvements have been made in this area, which include the use of multiple rather than single scoring functions and application of either consensus or multivariate statistical methods or both to improve the discrimination between binders and nonbinders. In view of it, the discriminative ability (distinguishing binders from nonbinders) of binary QSAR models derived using LUDI and MOE scoring functions has been compared with the models derived by Jacobbsson et al. on five data sets viz. estrogen receptor alphamimics (ERalpha_mimics), estrogen receptor alphatoxins (ERalpha_toxins), matrix metalloprotease 3 inhibitors (MMP-3), factor Xa inhibitors (fXa), and acetylcholine esterase inhibitors (AChE). The overall analyses reveal that binary QSAR is comparable to the PLS discriminant analysis, rule-based, and Bayesian classification methods used by Jacobsson et al. Further the scoring functions implemented in LUDI and MOE can score a wide range of protein-ligand interactions and are comparable to the scoring functions implemented in ICM and Cscore. Thus the binary QSAR models derived using LUDI and MOE scoring functions may be useful as a preliminary screening layer in a multilayered virtual screening paradigm.

Cholinesterase Inhibitors↗
PubMed2006

BAli-Phy: simultaneous Bayesian inference of alignment and phylogeny.

SUMMARY: BAli-Phy is a Bayesian posterior sampler that employs Markov chain Monte Carlo to explore the joint space of alignment and phylogeny given molecular sequence data. Simultaneous estimation eliminates bias toward inaccurate alignment guide-trees, employs more sophisticated substitution models during alignment and automatically utilizes information in shared insertion/deletions to help infer phylogenies. AVAILABILITY: Software is available for download at http://www.biomath.ucla.edu/msuchard/bali-phy.

Algorithms↗
PubMed2001

A Bayesian hierarchical model for allele frequencies.

Genetic epidemiological methodologies, such as linkage analysis, often require accurate estimates of allele frequencies. When studies involve multiple sub-populations with different evolutionary histories, accurate estimates can be difficult to obtain because the number of subjects per sub-population tends to be limited. Given allele counts for a collection of loci and sub-populations, we propose a Bayesian hierarchical model that extends existing empirical Bayesian approaches by allowing for explicit inclusion of prior information about both allele frequencies and inter-population divergence. We describe how such information can be derived from published data and then incorporated into the model via prior distributions for model parameters. By analysis of simulated data, we highlight how the hierarchical model, as implemented in the publicly available program AllDist, combines prior information with the observed data to refine allele frequency estimates.

Alleles↗
PubMed2006

Learning functional structure from fMR images.

We propose a novel method using Bayesian networks to learn the structure of effective connectivity among brain regions involved in a functional MR experiment. The approach is exploratory in the sense that it does not require an a priori model as in the earlier approaches, such as the Structural Equation Modeling or Dynamic Causal Modeling, which can only affirm or refute the connectivity of a previously known anatomical model or a hypothesized model. The conditional probabilities that render the interactions among brain regions in Bayesian networks represent the connectivity in the complete statistical sense. The present method is applicable even when the number of regions involved in the cognitive network is large or unknown. We demonstrate the present approach by using synthetic data and fMRI data collected in silent word reading and counting Stroop tasks.

Adult↗