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PubMed1992

Bayesian belief networks in quantitative histopathology.

Bayesian belief networks have a dynamic range and numeric response characteristics that make them uniquely suitable for descriptive classification schemes. Features showing considerable overlap of tolerance regions may be used, in a cumulative manner, to derive unequivocal classification decisions. The numeric response characteristics of Bayesian belief networks are analyzed, and their application as control modules in automated scene segmentation in histopathology is demonstrated.

Bayes Theorem↗
PubMedDate not supplied

Bayesian neural networks for aroma classification.

Bayesian Neural Networks (BNNs) are investigated to test their potential to distinguish between different aroma impressions. Special attention is thereby drawn on mixed aroma impressions, resulting from the flavor description of a single compound with more than one aroma quality. The structures of 133 pyrazine-derived aroma compounds as well as their aroma descriptions are selected for comparison. The information fed into the neural networks is based on molecular descriptors calculated from the geometrically optimized chemical structures. While in the case of the Probabilistic Neural Network (PNN) the networks' output consists of a categorical variable, the output for the General Regression Neural Network (GRNN) is defined in a numerical way. The best models attain comparable performance with a correct prediction of 90.8% of the cases for PNN and 89.9% for GRNN, respectively. Comparison of the BNN results to those obtained by Multiple Linear Regression (MLR) points out that the nonlinear methods work significantly better on the studied problem and that BNNs can be applied to multiple-category problems in structure-flavor relationships with good accuracy.

Bayes Theorem↗
PubMed2006

An effective structure learning method for constructing gene networks.

MOTIVATION: Bayesian network methods have shown promise in gene regulatory network reconstruction because of their capability of capturing causal relationships between genes and handling data with noises found in biological experiments. The problem of learning network structures, however, is NP hard. Consequently, heuristic methods such as hill climbing are used for structure learning. For networks of a moderate size, hill climbing methods are not computationally efficient. Furthermore, relatively low accuracy of the learned structures may be observed. The purpose of this article is to present a novel structure learning method for gene network discovery. RESULTS: In this paper, we present a novel structure learning method to reconstruct the underlying gene networks from the observational gene expression data. Unlike hill climbing approaches, the proposed method first constructs an undirected network based on mutual information between two nodes and then splits the structure into substructures. The directional orientations for the edges that connect two nodes are then obtained by optimizing a scoring function for each substructure. Our method is evaluated using two benchmark network datasets with known structures. The results show that the proposed method can identify networks that are close to the optimal structures. It outperforms hill climbing methods in terms of both computation time and predicted structure accuracy. We also apply the method to gene expression data measured during the yeast cycle and show the effectiveness of the proposed method for network reconstruction.

Algorithms↗
PubMed1999

Computer-assisted diagnosis of breast cancer using a data-driven Bayesian belief network.

This study investigates a simple Bayesian belief network for the diagnosis of breast cancer, and specifically addresses the question of whether integrating image and non-image based features into a single network can yield better performance than hybrid combinations of independent networks. From a dataset of 419 cases, including 92 malignancies, 13 features relating to mammographic findings, physical examinations and patients' clinical histories, were extracted to build three Bayesian belief networks. The scenarios tested included a network incorporating all features and two hybrids which combined the outputs of sub-networks corresponding to the image or non-image features. Average areas (Az) under the corresponding ROC curves were used as measures of performance. The network incorporating only image based features performed better (Az =0.81) than that using nonimage features (Az = 0.71). Both hybrid classifiers yielded better performance (Az =0.85 for averaging and Az = 0.87 for logistic regression), but neither hybrid was as accurate as the network incorporating all features (Az = 0.89). This preliminary study suggests that, like human observers who concurrently consider different types of information, a single classifier that simultaneously evaluates both image and non-image information can achieve better diagnostic performance than the hybrid combinations considered here.

Adult↗
PubMed1991

Algorithms for Bayesian belief-network precomputation.

Bayesian belief networks provide an intuitive and concise means of representing probabilistic relationships among the variables in expert systems. A major drawback to this methodology is its computational complexity. We present an introduction to belief networks, and describe methods for precomputing, or caching, part of a belief network based on metrics of probability and expected utility. These algorithms are examples of a general method for decreasing expected running time for probabilistic inference. We first present the necessary background, and then present algorithms for producing caches based on metrics of expected probability and expected utility. We show how these algorithms can be applied to a moderately complex belief network, and present directions for future research.

Algorithms↗
PubMed2002

Improving inter-observer agreement and certainty level in diagnosing and grading papillary urothelial neoplasms: usefulness of a Bayesian belief network.

BACKGROUND AND OBJECTIVE: A Bayesian belief network (BBN), as diagnostic decision support system, enables the processing of our knowledge of histopathology expressed in descriptive terms, words and concepts. The aim of this study was to evaluate the contribution of a BBN in the improvement of inter-observer agreement and certainty level in the diagnosis and grading of papillary urothelial neoplasms. MATERIALS: Inter-observer agreement and certainty level were investigated on 40 cases of non-invasive papillary urothelial neoplasms subdivided according to the WHO 1973 classification. There were 10 urothelial papillomas (UPs), 10 grade 1 papillary carcinomas (G1), 10 grade 2 papillary carcinomas (G2) and 10 grade 3 papillary carcinomas (G3). Five consecutive sessions were held with three observers (RMa, PC and MSt). Sessions A, B and D were based on the morphological evaluation of the specimens with a conventional light microscope only. In sessions C and E, a BBN was used in addition to the microscope. The BBN output was represented by four belief values for four possible diagnostic outcomes. These values ranged from 0.0 to 1.0, with the sum of the belief values being 1.0. Concerning the certainty level, a two-tier system of assessment was adopted in sessions A, B and D: certain versus less certain. In sessions C and E, a belief value equal to or greater than 0.65 was considered as equivalent to "certain". RESULTS: In session A, an all-encompassing or synthetic approach to decision-making was adopted. Agreement with the gold standard was seen in 60% (RMa), 55% (PC) and 65% (MSt) of cases, respectively. The level of subjective confidence was "certain" in 35%, 40% and 35% of cases, respectively. Better agreement-70% (RMa), 68% (PC) and 72% (MSt) of cases-was present in session B where an analytical approach based on the evaluation of a series of morphological features was used. The level of subjective confidence was "certain" in 45%, 50% and 55% of cases, respectively. In session C, where a BBN was utilised, a further increase in degree of agreement with the gold standard was observed, e.g. 85% (RMa), 80% (PC) and 86% (MSt) of cases, respectively. Levels of certainty or belief values were high. Decrease in both the level of agreement-60% (RMa), 62% (PC) and 65% (MSt) of cases-and certainty was seen in session D where the observers were left free to evaluate the cases morphologically without the constrain of either a synthetic or analytical approach. In session E, where the BBN was used again, the percentage of cases in agreement with the gold standard increased to 83% (RMa), 81% (PC) and 84% (MSt), respectively. Increase in certainty or belief was also seen. The difference of the results obtained in the sessions A, B and D with those seen in the BBN-based sessions (C and E) is statistically significant. CONCLUSIONS: Conventional morphological evaluation of papillary urothelial neoplasms is affected by inter-observer variability and, in many instances, by diagnostic uncertainty. The greatest difficulties are found with G1 and G2 cases. Improvement in inter-observer agreement and certainty level can be achieved with a BBN.

Bayes Theorem↗
PubMed2004

Software reliability prediction using recurrent neural network with Bayesian regularization.

A recurrent neural network modeling approach for software reliability prediction with respect to cumulative failure time is proposed. Our proposed network structure has the capability of learning and recognizing the inherent internal temporal property of cumulative failure time sequence. Further, by adding a penalty term of sum of network connection weights, Bayesian regularization is applied to our network training scheme to improve the generalization capability and lower the susceptibility of overfitting. The performance of our proposed approach has been tested using four real-time control and flight dynamic application data sets. Numerical results show that our proposed approach is robust across different software projects, and has a better performance with respect to both goodness-of-fit and next-step-predictability compared to existing neural network models for failure time prediction.

Algorithms↗
PubMed2006

Evaluation of a Bayesian decision network for diagnosing pyloric stenosis.

PURPOSE: Most infants undergoing an ultrasound to rule out pyloric stenosis will have a negative study, suggesting the low accuracy of clinical assessment. The purpose of this study was to evaluate the feasibility of using a Bayesian network to improve the accuracy of diagnosing pyloric stenosis. METHODS: Records of 118 infants undergoing an ultrasound to rule out pyloric stenosis were reviewed. Data from 88 (75%) infants were used to train a Bayesian decision network that predicted the probability of pyloric stenosis using risk factors, signs, and symptoms of the disease. The emergency department records of the remaining 28 (25%) infants were used to test the network. Two groups of pediatric surgeons and pediatric emergency medicine physicians were asked to predict the probability of pyloric stenosis in the testing set: (1) physicians using the network and (2) physicians using only emergency department records. Accuracy was evaluated using area under the ROC curve (discrimination) and Hosmer-Lemeshow (H-L) c-statistic (calibration). RESULTS: Physicians using the Bayesian decision network better predicted the probability of pyloric stenosis among infants in the testing set than those not using the network (ROC 0.973 vs 0.882; H-L c-statistic 3.9 [P > .05] vs 24.3 [P < .05]). Physicians using the network would have ordered 22% fewer ultrasounds and missed no cases of pyloric stenosis. CONCLUSIONS: The use of a Bayesian decision network may improve the accuracy of physicians diagnosing infants with possible pyloric stenosis. Use of this decision tool may safely reduce the need for imaging among infants with suspected pyloric stenosis.

Bayes Theorem↗
PubMed1996

A higher order Bayesian neural network with spiking units.

We treat a Bayesian confidence propagation neural network, primarily in a classifier context. The one-layer version of the network implements a naive Bayesian classifier, which requires the input attributes to be independent. This limitation is overcome by a higher order network. The higher order Bayesian neural network is evaluated on a real world task of diagnosing a telephone exchange computer. By introducing stochastic spiking units, and soft interval coding, it is also possible to handle uncertain as well as continuous valued inputs.

Action Potentials↗
PubMed2005

Maximum likelihood haplotyping for general pedigrees.

Haplotype data is valuable in mapping disease-susceptibility genes in the study of Mendelian and complex diseases. We present algorithms for inferring a most likely haplotype configuration for general pedigrees, implemented in the newest version of the genetic linkage analysis system SUPERLINK. In SUPERLINK, genetic linkage analysis problems are represented internally using Bayesian networks. The use of Bayesian networks enables efficient maximum likelihood haplotyping for more complex pedigrees than was previously possible. Furthermore, to support efficient haplotyping for larger pedigrees, we have also incorporated a novel algorithm for determining a better elimination order for the variables of the Bayesian network. The presented optimization algorithm also improves likelihood computations. We present experimental results for the new algorithms on a variety of real and semiartificial data sets, and use our software to evaluate MCMC approximations for haplotyping.

Algorithms↗
PubMed2002

Bayesian probabilistic network modeling of remifentanil and propofol interaction on wakeup time after closed-loop controlled anesthesia.

OBJECTIVE: Until now, the knowledge of combining anesthetics to obtain an adequate level of anesthesia and to economize wakeup time has been empirical and difficult to represent in quantitative models. Since there is no reason to expect that the effect of non-opioid and opioid anesthetics can be modeled in a simple linear manner, the use of a new computational approach with Bayesian belief network software is demonstrated. METHODS: A data set from a pharmacodynamic study was used where remifentanil was randomly given in three fixed target concentrations (2, 4, and 8 ng/ml) to 62 subjects. Target concentrations of propofol were controlled according to the closed-loop system feedback of the auditory evoked potential index to render modeling unbiased by the level of anesthesia. Time to open eyes was measured to represent wakeup time after surgery. The NETICA version 1.37 software was used on a personal computer for network building, validation, and prediction. RESULTS: After the learning phase, the network was used to generate a series of random cases whose probability distribution matches that of the compiled network. The sampling algorithms used are precise, so that the frequencies of the simulated cases will exactly approach the probabilities of the network and that of the data learned. The graphical display of the predicted wakeup time shows less variability but a more complex interaction pattern than with the unadjusted original data. CONCLUSIONS: Model building and evaluation with Bayesian networks does not depend on underlying linear relationships. Bayesian relationships represent true features of the represented data sample. Data may be sparse, uncertain, stochastic, or imprecise. Multiple platform software that is easy to use is increasingly available. Bayesian networks promise to be versatile tools for building valid, nonlinear, predictive instruments to further gain insight into the complex interaction of anesthetics.

Anesthesia Recovery Period↗
PubMed1997

Subjective breast cancer grading. Analyses of reproducibility after application of Bayesian belief networks.

OBJECTIVE: To examine the influence of Bayesian belief networks (BBNs) on the reproducibility of subjective breast cancer grading. STUDY DESIGN: Twenty samples were analyzed for intraobserver and 128 samples for interobserver reproducibility using the Bloom-Richardson and Helpap grading systems. The expression of diagnostic features was evaluated subjectively, and for each a decision it was determined to what extent it represented one of the different outcomes. Evidence was then entered, for each diagnostic feature, into four different BBNs, recently described for breast cancer grading, in the form of a relative likelihood ratio vector. RESULTS: With all cases considered, the use of decision support based on the Bloom-Richardson and Helpap grading systems did not improve intraobserver reproducibility. This was found to be 68% and 80% in subjective gradings, respectively, and 60% and 70% in the BBN-supported method. Interobserver reproducibility was not improved (58% and 70% in subjective gradings and 51-59% based on assessment with decision support). However, when only cases associated with high beliefs were considered, both intraobserver reproducibility (agreement rose from 68% to 93%) and interobserver reproducibility (agreement rose from 60% to 87%) of BBN-supported gradings exceeded the results of subjective assessments. CONCLUSION: The results showed that the observers did not reach the same diagnosis (or grade) and that their observational assessment of histologic features lacked agreement. Since BBNs reflected only the data entered, poor agreement existed in the contribution to the final diagnostic belief by the different features and, ultimately, in belief in the final decision.

Bayes Theorem↗
PubMedDate not supplied

Use of automatic relevance determination in QSAR studies using Bayesian neural networks.

We describe the use of Bayesian regularized artificial neural networks (BRANNs) coupled with automatic relevance determination (ARD) in the development of quantitative structure-activity relationship (QSAR) models. These BRANN-ARD networks have the potential to solve a number of problems which arise in QSAR modeling such as the following: choice of model; robustness of model; choice of validation set; size of validation effort; and optimization of network architecture. The ARD method ensures that irrelevant or highly correlated indices used in the modeling are neglected as well as showing which are the most important variables in modeling the activity data. The application of the methods to QSAR of compounds active at the benzodiazepine and muscarinic receptors as well as some toxicological data of the effect of substituted benzenes on Tetetrahymena pyriformis is illustrated.

Journal Article↗
PubMed2002

Combining location and expression data for principled discovery of genetic regulatory network models.

We develop principled methods for the automatic induction (discovery) of genetic regulatory network models from multiple data sources and data modalities. Models of regulatory networks are represented as Bayesian networks, allowing the models to compactly and robustly capture probabilistic multivariate statistical dependencies between the various cellular factors in these networks. We build on previous Bayesian network validation results by extending the validation framework to the context of model induction, leveraging heuristic simulated annealing search algorithms and posterior model averaging. Using expression data in isolation yields results inconsistent with location data so we incorporate genomic location data to guide the model induction process. We combine these two data modalities by allowing location data to influence the model prior and expression data to influence the model likelihood. We demonstrate the utility of this approach by discovering genetic regulatory models of thirty-three variables involved in S. cerevisiae pheromone response. The models we automatically generate are consistent with the current understanding regarding this regulatory network, but also suggest new directions for future experimental investigation.

Bayes Theorem↗
PubMed1993

A flexible and fault tolerant query-reply system based on a Bayesian neural network.

A query-reply system based on a Bayesian neural network is described. Strategies for generating questions which make the system both efficient and highly fault tolerant are presented. This involves having one phase of question generation intended to quickly reach a hypothesis followed by a phase where verification of the hypothesis is attempted. In addition, both phases have strategies for detecting and removing inconsistencies in the replies from the user. Also described is an explanatory mechanism which gives information related to why a certain hypotheses is reached or question asked. Specific examples of the systems behavior as well as the results of a statistical evaluation are presented.

Animals↗
PubMed1996

Androgen-deprived prostate adenocarcinoma: evaluation of treatment-related changes versus no distinctive treatment effect with a Bayesian belief network. A methodological approach.

OBJECTIVE: To develop and test a Bayesian belief network (BBN) for the identification of prostatic adenocarcinomas (PACs) with combination endocrine therapy (CET) changes from PACs with poor to no treatment CET effect and from untreated PACs. METHODS: A network was designed with a decision node containing three diagnostic alternatives (PAC with CET effect, PAC with poor to no treatment effect, and untreated PAC) and seven first-level evidence nodes for the diagnostic features: nuclear enlargement; frequency of prominent nucleoli; cell cytoplasm vacuolization; shrunken acini; individual infiltrating tumor cells; WHO prostate cancer pattern recognition, and amount of interstitial tissue stroma. Three prototype cases, one for each diagnostic alternative, were used to develop the BBN. The BBN performance was then evaluated in 40 prostatectomies for PAC, consisting of 20 CET treated and 20 untreated cases. RESULTS: The results obtained with the three prototypes showed that the network can identify the diagnostic alternatives with certainty when seven features are polled. When the performance was evaluated in the 40 PACs, the belief values were 1.0 or close to it in most of the cases (the value range is 0.0-1.0; the closer to 1.0, the greater the belief). Moreover, the BBN allowed an identification with high certainty of PACs with treatment-related changes from those either with poor to no treatment effect or untreated. CONCLUSIONS: A BBN for the evaluation of androgen-deprived PAC offers a descriptive classifier which is readily implemented and allows the use of descriptive, linguistic terms.

Adenocarcinoma↗
PubMed1995

Management of uncertainty in breast cancer grading with Bayesian belief networks.

OBJECTIVE: To examine the potential of different constructs of Bayesian belief networks (BBN) to manage uncertainty in breast cancer grading. STUDY DESIGN: We developed four networks, two based on Bloom-Richardson's and two on Helpap's grading systems. The function of the networks was based either on an expert's experience or frequency counts derived from subjective grading of a large number of samples. The four BBNs were tested on 20 specimens, and the resulting final beliefs were compared with the subjective gradings. RESULTS: The BBNs showed agreement with the subjective gradings in 60-85% of cases. Different constructs of BBNs, however, differed in their performance. The mean beliefs in frequency-based networks were slightly higher than in experience-based networks. In addition, as compared with the Bloom-Richardson-based networks, the Helpap-based BBNs resulted in higher maximum beliefs but produced a larger fraction of discrepancies with the subjectively graded cases. Depending on the type of network, 65-90% of the BBN grades were associated with high beliefs. CONCLUSION: The results suggest that for reliable results, grading systems with more than three or four variables may be necessary. When based on relevant information, BBNs seem to have the potential to become a valuable method of assisting the pathologist in breast cancer grading.

Breast Neoplasms↗
PubMed2006

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

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

Algorithms↗