Search PubMed⌕ Search

SEARCH · Search PubMed

Results for “Bayesian networks”

Search indexed PubMed citations on genomics, clinical trials, systematic reviews and public health. Explore titles, authors and supplied subject terms, then open the PubMed record.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 127 records · Page 7Linked to original sources
PubMed2004

Bayesian network multi-classifiers for protein secondary structure prediction.

Successful secondary structure predictions provide a starting point for direct tertiary structure modelling, and also can significantly improve sequence analysis and sequence-structure threading for aiding in structure and function determination. Hence the improvement of predictive accuracy of the secondary structure prediction becomes essential for future development of the whole field of protein research. In this work we present several multi-classifiers that combine the predictions of the best current classifiers available on Internet. Our results prove that combining the predictions of a set of classifiers by creating composite classifiers is a fruitful one. We have created multi-classifiers that are more accurate than any of the component classifiers. The multi-classifiers are based on Bayesian networks. They are validated with 9 different datasets. Their predictive accuracy results outperform the best secondary structure predictors by 1.21% on average. Our main contributions are: (i) we improved the best know predictive accuracy by 1.21%, (ii) our best results have been obtained with a new semi naïve Bayes approach named Pazzani-EDA and (iii) our multi-classifiers combine results of previously build classifiers predictions obtained through Internet, thanks to our development of a Java application.

Bayes Theorem↗
PubMed2026

Comparative Efficacy of Non-opioid Analgesic Drugs for Chronic Cancer Pain: A Bayesian Network Meta-analysis.

PURPOSE: While opioids remain the primary pharmacological intervention for cancer pain management, their clinical utility is frequently compromised by dose-limiting toxicities. This study aimed to determine the comparative efficacy, opioid-sparing potential, and clinical hierarchy of non-opioid adjuvant drug classes. The study was structured around the PICO framework to evaluate the pharmacological strategies currently utilized in multimodal clinical oncology. METHODS: A systematic search of electronic databases (PubMed, Embase, Cochrane) was conducted for randomized controlled trials (RCTs) published between 2000 and 2025. The primary outcome was global analgesic efficacy (standardized mean difference [SMD]), while secondary outcomes included the opioid-sparing effect, defined as the percentage reduction in morphine equivalent daily dose (MEDD) and the incidence of treatment-emergent adverse events (Harms). A Bayesian network meta-analysis (NMA) was performed to rank treatments using SUCRA values. The methodological quality was assessed using the Cochrane Risk of Bias (RoB 2.0) tool. RESULTS: Twenty-three RCTs (n = 1845) met the inclusion criteria. Nonsteroidal anti-inflammatory drugs (NSAIDs) (-1.10) and anticonvulsants (-1.06) demonstrated the most robust analgesic effects. The SUCRA ranking confirmed a clear hierarchy, with the combination of anticonvulsants and antidepressants showing the highest probability of efficacy. A significant opioid-sparing effect was observed for gabapentinoids and ketamine, facilitating MEDD reduction. While serious adverse events were rare, minor harms (somnolence, dizziness) were more frequent in the most effective classes. CONCLUSION: Our NMA provides a robust evidence base for a "Clinical Tier" system, ranking adjuvants by their balance of efficacy and safety. These findings support the early integration of Tier I agents (anticonvulsants and NSAIDs) to optimize pain control and reduce opioid-related toxicities in chronic cancer pain management.

Humans↗
PubMed2004

Using a Bayesian network to predict the probability and type of breast cancer represented by microcalcifications on mammography.

Since the widespread adoption of mammographic screening in the 1980's there has been a significant increase in the detection and biopsy of both benign and malignant microcalcifications. Though current practice standards recommend that the positive predictive value (PPV) of breast biopsy should be in the range of 25-40%, there exists significant variability in practice. Microcalcifications, if malignant, can represent either a non-invasive or an invasive form of breast cancer. The distinction is critical because distinct surgical therapies are indicated. Unfortunately, this information is not always available at the time of surgery due to limited sampling at image-guided biopsy. For these reasons we conducted an experiment to determine whether a previously created Bayesian network for mammography could predict the significance of microcalcifications. In this experiment we aim to test whether the system is able to perform two related tasks in this domain: 1) to predict the likelihood that microcalcifications are malignant and 2) to predict the likelihood that a malignancy is invasive to help guide the choice of appropriate surgical therapy.

Bayes Theorem↗
PubMed2002

Interpreting small quantities of DNA: the hierarchy of propositions and the use of Bayesian networks.

The dramatic increase in the sensitivity of DNA profiling systems that has occurred over recent years has led to the need to address a wider range of interpretational problems in forensic science. The issues surrounding questions of the kind "whose DNA is this?" have been the subject of considerable controversy but now it is clear that the emphasis is shifting to questions of the kind "how did this DNA get here?" Such issues are discussed in this paper and new insights are provided by two particular recent developments. First, the notion of the "hierarchy of propositions" that has arisen from a project called Case Assessment and Interpretation (CAI) that has been running in the British Forensic Science Service (FSS). Second, a technique for drawing inferences in the face of many interacting considerations, known as "Bayesian networks"--or "Bayes' nets" for short--that has been the subject of an earlier paper in this journal (1). The discussion is carried out by means of case studies, based on actual cases. It is clear that, whereas the inference in relation to the source of the DNA in a crime sample might be overwhelmingly strong, the inference in relation to the propositions that a jury must consider relating to the identity of the actual offender may be much more tentative.

Bayes Theorem↗
PubMed2025

Comparative Efficacy of Janus Kinase Inhibitors Indicated for Severe Alopecia Areata: A Bayesian Network Meta-Analysis and Matching-Adjusted Indirect Comparison.

Systemic Janus kinase inhibitors (JAKIs) have markedly advanced the therapeutic landscape for alopecia areata (AA). Although baricitinib and ritlecitinib are approved in the United States (US) and Europe, and deuruxolitinib in the US for severe AA, the lack of head-to-head randomized controlled trials (RCTs) limits evidence-based prescribing decisions. Moreover, prior meta-analyses excluded data on certain oral JAKIs or incorporated findings from agents and dosing regimens that were abandoned, investigational, clinically ineffective, or associated with unacceptable safety profiles. To compare the efficacy of oral JAKIs, limited to FDA, EMA, or MHRA approved drugs and doses-baricitinib (2 and 4 mg QD), ritlecitinib (50 mg QD), and deuruxolitinib (8 mg BID)-for severe AA, using advanced indirect comparison methodologies. A systematic review was performed following PRISMA 2020 guidelines (CRD420251116775). Bayesian network meta-analysis (NMA) synthesized data from RCTs reporting Week 24 outcomes on Severity of Alopecia Tool (SALT) ≤ 10 and SALT ≤ 20 thresholds. Multilevel network meta-regression (ML-NMR) evaluated heterogeneity and adjusted for baseline imbalances. Additionally, unanchored matching-adjusted indirect comparisons (MAIC) were conducted using individual patient-level data from THRIVE trials. Surface under the cumulative ranking (SUCRA) values were calculated to rank treatments. Seven RCTs (n = 4560 participants) were included. Deuruxolitinib 8 mg significantly outperformed baricitinib 2 and 4 mg on both SALT endpoints. Differences with ritlecitinib 50 mg were directionally favorable for deuruxolitinib but not statistically significant in NMA and ML-NMR models. MAICs confirmed superior odds for deuruxolitinib versus baricitinib 2 mg (OR = 71.55) and ritlecitinib (OR = 18.27) for SALT ≤ 20. SUCRA rankings also consistently favored deuruxolitinib. Among approved oral JAKIs, deuruxolitinib 8 mg shows the highest short-term efficacy for severe AA. These findings provide preliminary evidence to guide treatment decisions but should be interpreted as exploratory pending confirmation.

Humans↗
PubMed2006

Using complexity for the estimation of Bayesian networks.

Statistical inference of graphical models has become an important tool in the reconstruction of biological networks of the type which model, for example, gene regulatory interactions. In particular, the construction of a score-based Bayesian posterior density over the space of models provides an intuitive and computationally feasible method of assessing model uncertainty and of assigning statistical confidence to structural features. One problem which frequently occurs with this approach is the tendency to overestimate the degree of model complexity. Spurious graphical features obtained in this way may affect the inference in unpredictable ways, even when using scoring techniques, such as the Bayesian Information Criterion (BIC), that are specifically designed to compensate for overfitting. In this article we propose a simple adjustment to a BIC-based scoring procedure. The method proceeds in two steps. In the first step we derive an independent estimate of the parametric complexity of the model. In the second we modify the BIC score so that the mean parametric complexity of the posterior density is equal to the estimated value. The method is applied to a set of test networks, and to a collection of genes from the yeast genome known to possess regulatory relationships. A Bayesian network model with binary responses is employed. In the examples considered, we find that the number of spurious graph edges inferred is reduced, while the effect on the identification of true edges is minimal.

Algorithms↗
PubMed2000

A Bayesian network for mammography.

The interpretation of a mammogram and decisions based on it involve reasoning and management of uncertainty. The wide variation of training and practice among radiologists results in significant variability in screening performance with attendant cost and efficacy consequences. We have created a Bayesian belief network to integrate the findings on a mammogram, based on the standardized lexicon developed for mammography, the Breast Imaging Reporting And Data System (BI-RADS). Our goal in creating this network is to explore the probabilistic underpinnings of this lexicon as well as standardize mammographic decision-making to the level of expert knowledge.

Adult↗
PubMed2005

A spatio-temporal Bayesian network classifier for understanding visual field deterioration.

OBJECTIVE: Progressive loss of the field of vision is characteristic of a number of eye diseases such as glaucoma which is a leading cause of irreversible blindness in the world. Recently, there has been an explosion in the amount of data being stored on patients who suffer from visual deterioration including field test data, retinal image data and patient demographic data. However, there has been relatively little work in modelling the spatial and temporal relationships common to such data. In this paper we introduce a novel method for classifying visual field (VF) data that explicitly models these spatial and temporal relationships. METHODOLOGY: We carry out an analysis of our proposed spatio-temporal Bayesian classifier and compare it to a number of classifiers from the machine learning and statistical communities. These are all tested on two datasets of VF and clinical data. We investigate the receiver operating characteristics curves, the resulting network structures and also make use of existing anatomical knowledge of the eye in order to validate the discovered models. RESULTS: Results are very encouraging showing that our classifiers are comparable to existing statistical models whilst also facilitating the understanding of underlying spatial and temporal relationships within VF data. The results reveal the potential of using such models for knowledge discovery within ophthalmic databases, such as networks reflecting the 'nasal step', an early indicator of the onset of glaucoma. CONCLUSION: The results outlined in this paper pave the way for a substantial program of study involving many other spatial and temporal datasets, including retinal image and clinical data.

Algorithms↗
PubMed2004

Reconstruction of gene networks using Bayesian learning and manipulation experiments.

MOTIVATION: The analysis of high-throughput experimental data, for example from microarray experiments, is currently seen as a promising way of finding regulatory relationships between genes. Bayesian networks have been suggested for learning gene regulatory networks from observational data. Not all causal relationships can be inferred from correlation data alone. Often several equivalent but different directed graphs explain the data equally well. Intervention experiments where genes are manipulated can help to narrow down the range of possible networks. RESULTS: We describe an active learning algorithm that suggests an optimized sequence of intervention experiments. Simulation experiments show that our selection scheme is better than an unguided choice of interventions in learning the correct network and compares favorably in running time and results with methods based on value of information calculations.

Algorithms↗
PubMed2003

Towards a decision support system for health promotion in nursing.

AIMS: This study was designed to investigate what type of models, techniques and data are necessary to support the development of a decision support system for health promotion practice in nursing. Specifically, the research explored how interview data can be interpreted in terms of Concept Networks and Bayesian Networks, both of which provide formal methods for describing the dependencies between factors or variables in the context of decision-making in health promotion. BACKGROUND: In nursing, the lack of generally accepted examples or guidelines by which to implement or evaluate health promotion practice is a challenge. Major gaps have been identified between health promotion rhetoric and practice and there is a need for health promotion to be presented in ways that make its attitudes and practices more easily understood. New tools, paradigms and techniques to encourage the practice of health promotion would appear to be beneficial. Concept Networks and Bayesian Networks are techniques that may assist the research team to understand and explicate health promotion more specifically and formally than has been the case, so that it may more readily be integrated into nursing practice. METHODS: As the ultimate goal of the study was to investigate ways to use the techniques described above, it was necessary to first generate data as text. Textual descriptions of health promotion in nursing were derived from in-depth qualitative interviews with nurses nominated by their peers as expert health promoting practitioners. FINDINGS: The nurses in this study gave only general and somewhat vague outlines of the concepts and ideas that guided their practice. These data were compared with descriptions from various sources that describe health promotion practices in nursing, then examples of a Conceptual Network and a representative Bayesian Network were derived from the data. CONCLUSIONS: The study highlighted the difficulty in describing health promotion practice, even among nurses recognized for their expertise in health promotion. Nevertheless, it indicated the data collection and analysis methods necessary to explicate the cognitive processes of health promotion and highlighted the benefits of using formal conceptualization techniques to improve health promotion practice.

Bayes Theorem↗
PubMedDate not supplied

Bias reduction in skewed binary classification with Bayesian neural networks.

The Bayesian evidence framework has become a standard of good practice for neural network estimation of class conditional probabilities. In this approach the conditional probability is marginalised over the distribution of network weights, which is usually approximated by an analytical expression that moderates the network output towards the midrange. In this paper, it is shown that the network calibration is considerably improved by marginalising to the prior distribution. Moreover, marginalisation to the midrange can seriously bias the estimates of the conditional probabilities calculated from the evidence framework. This is especially the case in the modelling of censored data.

Bayes Theorem↗
PubMedDate not supplied

A neural network approach to approximating MAP in belief networks.

Bayesian belief networks (BBN) are a widely studied graphical model for representing uncertainty and probabilistic interdependence among variables. One of the factors that restricts the model's wide acceptance in practical applications is that the general inference with BBN is NP-hard. This is also true for the maximum a posteriori probability (MAP) problem, which is to find the most probable joint value assignment to all uninstantiated variables, given instantiation of some variables in a BBN. To circumvent the difficulty caused by MAP's computational complexity, we suggest in this paper a neural network approximation approach. With this approach, a BBN is treated as a neural network without any change or transformation of the network structure, and the node activation functions are derived based on an energy function defined over a given BBN. Three methods are developed. They are the hill-climbing style discrete method, the simulated annealing method, and the continuous method based on the mean field theory. All three methods are for BBN of general structures, with the restriction that nodes of BBN are binary variables. In addition, rules for applying these methods to noisy-or networks are also developed, which may lead to more efficient computation in some cases. These methods' convergence is analyzed, and their validity tested through a series of computer experiments with two BBN of moderate size and complexity. Although additional theoretical and empirical work is needed, the analysis and experiments suggest that this approach may lead to effective and accurate approximation for MAP problems.

Algorithms↗
PubMed2006

Different classification techniques considering brain computer interface applications.

In this work the application of different machine learning techniques for classification of mental tasks from electroencephalograph (EEG) signals is investigated. The main application for this research is the improvement of brain computer interface (BCI) systems. For this purpose, Bayesian graphical network, neural network, Bayesian quadratic, Fisher linear and hidden Markov model classifiers are applied to two known EEG datasets in the BCI field. The Bayesian network classifier is used for the first time in this work for classification of EEG signals. The Bayesian network appeared to have a significant accuracy and more consistent classification compared to the other four methods. In addition to classical correct classification accuracy criteria, the mutual information is also used to compare the classification results with other BCI groups.

Algorithms↗
PubMed2001

Comparison of statistical methods for identification of Streptococcus thermophilus, Enterococcus faecalis, and Enterococcus faecium from randomly amplified polymorphic DNA patterns.

Thermophilic streptococci play an important role in the manufacture of many European cheeses, and a rapid and reliable method for their identification is needed. Randomly amplified polymorphic DNA (RAPD) PCR (RAPD-PCR) with two different primers coupled to hierarchical cluster analysis has proven to be a powerful tool for the classification and typing of Streptococcus thermophilus, Enterococcus faecium, and Enterococcus faecalis (G. Moschetti, G. Blaiotta, M. Aponte, P. Catzeddu, F. Villani, P. Deiana, and S. Coppola, J. Appl. Microbiol. 85:25-36, 1998). In order to develop a fast and inexpensive method for the identification of thermophilic streptococci, RAPD-PCR patterns were generated with a single primer (XD9), and the results were analyzed using artificial neural networks (Multilayer Perceptron, Radial Basis Function network, and Bayesian network) and multivariate statistical techniques (cluster analysis, linear discriminant analysis, and classification trees). Cluster analysis allowed the identification of S. thermophilus but not of enterococci. A Bayesian network proved to be more effective than a Multilayer Perceptron or a Radial Basis Function network for the identification of S. thermophilus, E. faecium, and E. faecalis using simplified RAPD-PCR patterns (obtained by summing the bands in selected areas of the patterns). The Bayesian network also significantly outperformed two multivariate statistical techniques (linear discriminant analysis and classification trees) and proved to be less sensitive to the size of the training set and more robust in the response to patterns belonging to unknown species.

Bayes Theorem↗
PubMed1995

Clinical applications of Bayesian belief networks in pathology.

Bayesian belief networks (BBNs) are a novel tool for representing knowledge about diagnostic decision making and for obtaining a numerical measure of certainty in the final diagnosis. Belief networks have been applied to the pathological assessment of breast, prostate and skin lesions and have been shown to provide consistency in the grading of microscopic features and improve diagnosis. These applications are reviewed in the current paper. The application of BBNs has been further facilitated through the use of standardised imagery which is stored digitally and used to enter evidence into a BBN. It is predicted that the further development of BBNs with improved logical capabilities represent the key to improved decision making in pathology.

Bayes Theorem↗