Searching for Bayesian Network Structures in the Space of Restricted Acyclic Partially Directed Graphs

Although many algorithms have been designed to construct Bayesian network structures using different approaches and principles, they all employ only two methods: those based on independence criteria, and those based on a scoring function and a search procedure (although some methods combine the two). Within the score+search paradigm, the dominant approach uses local search methods in the space of directed acyclic graphs (DAGs), where the usual choices for defining the elementary modifications (local changes) that can be applied are arc addition, arc deletion, and arc reversal. In this paper, we propose a new local search method that uses a different search space, and which takes account of the concept of equivalence between network structures: restricted acyclic partially directed graphs (RPDAGs). In this way, the number of different configurations of the search space is reduced, thus improving efficiency. Moreover, although the final result must necessarily be a local optimum given the nature of the search method, the topology of the new search space, which avoids making early decisions about the directions of the arcs, may help to find better local optima than those obtained by searching in the DAG space. Detailed results of the evaluation of the proposed search method on several test problems, including the well-known Alarm Monitoring System, are also presented

Ranking structured documents using utility theory in the Bayesian network retrieval model

In this paper a new method based on Utility and Decision theory is presented to deal with structured documents. The aim of the application of these methodologies is to refine a first ranking of structural units, generated by means of an Information Retrieval Model based on Bayesian Networks. Units are newly arranged in the new ranking by combining their posterior probabilities, obtained in the first stage, with the expected utility of retrieving them. The experimental work has been developed using the Shakespeare structured collection and the results show an improvement of the effectiveness of this new approach

A multi-layered Bayesian network model for structured document retrieval

New standards in document representation, like for example SGML, XML, and MPEG-7, compel Information Retrieval to design and implement models and tools to index, retrieve and present documents according to the given document structure. The paper presents the design of an Information Retrieval system for multimedia structured documents, like for example journal articles, e-books, and MPEG-7 videos. The system is based on Bayesian Networks, since this class of mathematical models enable to represent and quantify the relations between the structural components of the document. Some preliminary results on the system implementation are also presented

Why do women invest in pre-pregnancy health and care? A qualitative investigation with women attending maternity services

Background Despite the importance attributed to good pre-pregnancy care and its potential to improve pregnancy and child health outcomes, relatively little is known about why women invest in pre-pregnancy health and care. We sought to gain insight into why women invested in pre-pregnancy health and care. Methods We carried out 20 qualitative in-depth interviews with pregnant or recently pregnant women who were drawn from a survey of antenatal clinic attendees in London, UK. Interviewees were purposively sampled to include high and low investors in pre-pregnancy health and care, with variation in age, partnership status, ethnicity and pre-existing medical conditions. Data analysis was conducted using the Framework method. Results We identified three groups in relation to pre-pregnancy health and care: 1) The “prepared” group, who had high levels of pregnancy planning and mostly positive attitudes to micronutrient supplementation outside of pregnancy, carried out pre-pregnancy activities such as taking folic acid and making changes to diet and lifestyle. 2) The “poor knowledge” group, who also had high levels of pregnancy planning, did not carry out pre-pregnancy activities and described themselves as having poor knowledge. Elsewhere in their interviews they expressed a strong dislike of micronutrient supplementation. 3) The “absent pre-pregnancy period” group, had the lowest levels of pregnancy planning and also expressed anti-supplement views. Even discussing the pre-pregnancy period with this group was difficult as responses to questions quickly shifted to focus on pregnancy itself. Knowledge of folic acid was poor in all groups. Conclusion Different pre-pregnancy care approaches are likely to be needed for each of the groups. Among the “prepared” group, who were proactive and receptive to health messages, greater availability of information and better response from health professionals could improve the range of pre-pregnancy activities carried out. Among the “poor knowledge” group, better response from health professionals might yield greater uptake of pre-pregnancy information. A different, general health strategy might be more appropriate for the “absent pre-pregnancy period” group. The fact that general attitudes to micronutrient supplementation were closely related to whether or not women invested in pre-pregnancy health and care was an unanticipated finding and warrants further investigation.This report is independent research commissioned and funded by the Department of Health Policy Research Programme Pre-Pregnancy Health and Care in England: Exploring Implementation and Public Health Impact, 006/0068

Seeded Bayesian Networks: Constructing genetic networks from microarray data

<p>Abstract</p> <p>Background</p> <p>DNA microarrays and other genomics-inspired technologies provide large datasets that often include hidden patterns of correlation between genes reflecting the complex processes that underlie cellular metabolism and physiology. The challenge in analyzing large-scale expression data has been to extract biologically meaningful inferences regarding these processes – often represented as networks – in an environment where the datasets are often imperfect and biological noise can obscure the actual signal. Although many techniques have been developed in an attempt to address these issues, to date their ability to extract meaningful and predictive network relationships has been limited. Here we describe a method that draws on prior information about gene-gene interactions to infer biologically relevant pathways from microarray data. Our approach consists of using preliminary networks derived from the literature and/or protein-protein interaction data as seeds for a Bayesian network analysis of microarray results.</p> <p>Results</p> <p>Through a bootstrap analysis of gene expression data derived from a number of leukemia studies, we demonstrate that seeded Bayesian Networks have the ability to identify high-confidence gene-gene interactions which can then be validated by comparison to other sources of pathway data.</p> <p>Conclusion</p> <p>The use of network seeds greatly improves the ability of Bayesian Network analysis to learn gene interaction networks from gene expression data. We demonstrate that the use of seeds derived from the biomedical literature or high-throughput protein-protein interaction data, or the combination, provides improvement over a standard Bayesian Network analysis, allowing networks involving dynamic processes to be deduced from the static snapshots of biological systems that represent the most common source of microarray data. Software implementing these methods has been included in the widely used TM4 microarray analysis package.</p

Identifying significant genetic regulatory networks in the prostate cancer from microarray data based on transcription factor analysis and conditional independency

<p>Abstract</p> <p>Background</p> <p>Prostate cancer is a world wide leading cancer and it is characterized by its aggressive metastasis. According to the clinical heterogeneity, prostate cancer displays different stages and grades related to the aggressive metastasis disease. Although numerous studies used microarray analysis and traditional clustering method to identify the individual genes during the disease processes, the important gene regulations remain unclear. We present a computational method for inferring genetic regulatory networks from micorarray data automatically with transcription factor analysis and conditional independence testing to explore the potential significant gene regulatory networks that are correlated with cancer, tumor grade and stage in the prostate cancer.</p> <p>Results</p> <p>To deal with missing values in microarray data, we used a K-nearest-neighbors (KNN) algorithm to determine the precise expression values. We applied web services technology to wrap the bioinformatics toolkits and databases to automatically extract the promoter regions of DNA sequences and predicted the transcription factors that regulate the gene expressions. We adopt the microarray datasets consists of 62 primary tumors, 41 normal prostate tissues from Stanford Microarray Database (SMD) as a target dataset to evaluate our method. The predicted results showed that the possible biomarker genes related to cancer and denoted the androgen functions and processes may be in the development of the prostate cancer and promote the cell death in cell cycle. Our predicted results showed that sub-networks of genes SREBF1, STAT6 and PBX1 are strongly related to a high extent while ETS transcription factors ELK1, JUN and EGR2 are related to a low extent. Gene SLC22A3 may explain clinically the differentiation associated with the high grade cancer compared with low grade cancer. Enhancer of Zeste Homolg 2 (EZH2) regulated by RUNX1 and STAT3 is correlated to the pathological stage.</p> <p>Conclusions</p> <p>We provide a computational framework to reconstruct the genetic regulatory network from the microarray data using biological knowledge and constraint-based inferences. Our method is helpful in verifying possible interaction relations in gene regulatory networks and filtering out incorrect relations inferred by imperfect methods. We predicted not only individual gene related to cancer but also discovered significant gene regulation networks. Our method is also validated in several enriched published papers and databases and the significant gene regulatory networks perform critical biological functions and processes including cell adhesion molecules, androgen and estrogen metabolism, smooth muscle contraction, and GO-annotated processes. Those significant gene regulations and the critical concept of tumor progression are useful to understand cancer biology and disease treatment.</p

Efficient total syntheses and biological activities of two teixobactin analogues

The discovery of the new antibiotic teixobactin has been timely in the race for unearthing novel antibiotics wherein the emergence of drug resistance bacteria poses a serious threat worldwide. Herein, we present the total syntheses and biological activities of two teixobactin analogues. This approach is simple, efficient and has several advantages: it uses commercially available building blocks (except AllocHN-D-Thr-OH), has a single purification step and a good recovery (22). By using this approach we have synthesised two teixobactin analogues and established that the D-amino acids are critical for the antimicrobial activity of these analogues. With continuing high expectations from teixobactin, this work can be regarded as a stepping stone towards an in depth study of teixobactin, its analogues and the quest for synthesising similar molecules

Association of uric acid with kidney function and albuminuria: the Uric Acid Right for heArt Health (URRAH) Project

Background: Hyperuricemia is commonly observed in patients with chronic kidney disease (CKD). However, a better understanding of the relationship among uric acid (UA) values, glomerular filtration rate (GFR) and albuminuria may shed light on the mechanisms underlying the excess of cardiovascular mortality associated with both chronic kidney disease and hyperuricemia and lead to better risk stratification. Our main goal was to study the relationships between serum uric acid and kidney disease measures (namely estimated GFR [eGFR] and albuminuria) in a large cohort of individuals at cardiovascular risk from the URic acid Right for heArt Health (URRAH) Project database. Methods: Clinical data of 26,971 individuals were analyzed. Factors associated with the presence of hyperuricemia defined on the basis of previously determined URRAH cutoffs for cardiovascular and all-cause mortality were evaluated through multivariate analysis. Chronic kidney disease was defined as eGFR &lt; 60 ml/min per 1.73 m2 and/or abnormal urinary albumin excretion diagnosed as: (i) microalbuminuria if urinary albumin concentration was &gt; 30 and ≤ 300 mg/L, or if urinary albumin-to-creatinine ratio (ACR) was &gt; 3.4 mg/mmol and ≤ 34 mg/mmol; (ii) macroalbuminuria if urinary albumin concentration was &gt; 300 mg/L, or if ACR was &gt; 34 mg/mmol. Results: Mean age was 58 ± 15 years (51% males, 62% with hypertension and 12% with diabetes), mean eGFR was 81 ml/min per 1.73m22with a prevalence of eGFR &lt; 60 and micro- or macroalbuminuria of 16, 15 and 4%, respectively. Serum uric acid showed a trend towards higher values along with decreasing renal function. Both the prevalence of gout and the frequency of allopurinol use increased significantly with the reduction of eGFR and the increase in albuminuria. Hyperuricemia was independently related to male gender, eGFR strata, and signs of insulin resistance such as body mass index (BMI) and triglycerides. Conclusions: The lower the eGFR the higher the prevalence of hyperuricemia and gout. In subjects with eGFR &lt; 60 ml/min the occurrence of hyperuricemia is about 10 times higher than in those with eGFR &gt; 90 ml/min. The percentage of individuals treated with allopurinol was below 2% when GFR was above 60 ml/min, it increased to 20% in the presence of CKD 3b and rose further to 35% in individuals with macroalbuminuria