scholarly journals Path-level interpretation of Gaussian graphical models using the pair-path subscore

2022 ◽  
Vol 23 (1) ◽  
Author(s):  
Nathan P. Gill ◽  
Raji Balasubramanian ◽  
James R. Bain ◽  
Michael J. Muehlbauer ◽  
William L. Lowe ◽  
...  
Keyword(s):  
Graphical Models ◽  
Structural Information ◽  
Adverse Pregnancy Outcome ◽  
Pearson Correlation ◽  
R Package ◽  
Biological Data ◽  
Gaussian Graphical Models ◽  
Cross Sectional ◽  
Metabolomics Data ◽  
Adverse Pregnancy

Abstract Background  Construction of networks from cross-sectional biological data is increasingly common. Many recent methods have been based on Gaussian graphical modeling, and prioritize estimation of conditional pairwise dependencies among nodes in the network. However, challenges remain on how specific paths through the resultant network contribute to overall ‘network-level’ correlations. For biological applications, understanding these relationships is particularly relevant for parsing structural information contained in complex subnetworks. Results We propose the pair-path subscore (PPS), a method for interpreting Gaussian graphical models at the level of individual network paths. The scoring is based on the relative importance of such paths in determining the Pearson correlation between their terminal nodes. PPS is validated using human metabolomics data from the Hyperglycemia and adverse pregnancy outcome (HAPO) study, with observations confirming well-documented biological relationships among the metabolites. We also highlight how the PPS can be used in an exploratory fashion to generate new biological hypotheses. Our method is implemented in the R package , available at https://github.com/nathan-gill/pps. Conclusions The PPS can be used to probe network structure on a finer scale by investigating which paths in a potentially intricate topology contribute most substantially to marginal behavior. Adding PPS to the network analysis toolkit may enable researchers to ask new questions about the relationships among nodes in network data.

scholarly journals Gaussian graphical modeling of the serum exposome and metabolome reveals interactions between environmental chemicals and endogenous metabolites

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Vincent Bessonneau ◽  
Roy R. Gerona ◽  
Jessica Trowbridge ◽  
Rachel Grashow ◽  
Thomas Lin ◽  
...  
Keyword(s):  
Graphical Models ◽  
Association Studies ◽  
Environmental Chemicals ◽  
Gaussian Graphical Models ◽  
Metabolomics Data ◽  
Health And Nutrition ◽  
Novel Approach ◽  
Disease Causation ◽  
Women Firefighters ◽  
Endogenous Metabolites

AbstractGiven the complex exposures from both exogenous and endogenous sources that an individual experiences during life, exposome-wide association studies that interrogate levels of small molecules in biospecimens have been proposed for discovering causes of chronic diseases. We conducted a study to explore associations between environmental chemicals and endogenous molecules using Gaussian graphical models (GGMs) of non-targeted metabolomics data measured in a cohort of California women firefighters and office workers. GGMs revealed many exposure-metabolite associations, including that exposures to mono-hydroxyisononyl phthalate, ethyl paraben and 4-ethylbenzoic acid were associated with metabolites involved in steroid hormone biosynthesis, and perfluoroalkyl substances were linked to bile acids—hormones that regulate cholesterol and glucose metabolism—and inflammatory signaling molecules. Some hypotheses generated from these findings were confirmed by analysis of data from the National Health and Nutrition Examination Survey. Taken together, our findings demonstrate a novel approach to discovering associations between chemical exposures and biological processes of potential relevance for disease causation.

DiffNetFDR: differential network analysis with false discovery rate control

2019 ◽  
Vol 35 (17) ◽  
pp. 3184-3186
Author(s):  
Xiao-Fei Zhang ◽  
Le Ou-Yang ◽  
Shuo Yang ◽  
Xiaohua Hu ◽  
Hong Yan
Keyword(s):  
False Discovery Rate ◽  
Graphical Models ◽  
Biological Significance ◽  
R Package ◽  
Supplementary Information ◽  
Gaussian Graphical Models ◽  
Multiple Testing Procedure ◽  
False Discovery ◽  
Differential Network ◽  
Shiny App

Abstract Summary To identify biological network rewiring under different conditions, we develop a user-friendly R package, named DiffNetFDR, to implement two methods developed for testing the difference in different Gaussian graphical models. Compared to existing tools, our methods have the following features: (i) they are based on Gaussian graphical models which can capture the changes of conditional dependencies; (ii) they determine the tuning parameters in a data-driven manner; (iii) they take a multiple testing procedure to control the overall false discovery rate; and (iv) our approach defines the differential network based on partial correlation coefficients so that the spurious differential edges caused by the variants of conditional variances can be excluded. We also develop a Shiny application to provide easier analysis and visualization. Simulation studies are conducted to evaluate the performance of our methods. We also apply our methods to two real gene expression datasets. The effectiveness of our methods is validated by the biological significance of the identified differential networks. Availability and implementation R package and Shiny app are available at https://github.com/Zhangxf-ccnu/DiffNetFDR. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Reference Values of Fetal Peak Systolic Velocity in the Middle Cerebral Artery at 19–40 Weeks of Gestation in Nepalese Population

2014 ◽  
Vol 8 (2) ◽  
pp. 42-45 ◽  
Author(s):  
U Shrestha ◽  
I Shrestha ◽  
RK Ghimire ◽  
S Paudel
Keyword(s):  
Middle Cerebral Artery ◽  
Cerebral Artery ◽  
Gestational Age ◽  
Adverse Pregnancy Outcome ◽  
Peak Systolic Velocity ◽  
Cross Sectional Study ◽  
Cross Sectional ◽  
Systolic Volume ◽  
Serial Measurement ◽  
Adverse Pregnancy

Aims: The purpose of this study was to construct new reference range for fetal middle cerebral artery peak systolic velocity (MCA-PSV) in uncomplicated pregnancy at 19-40 weeks of gestation. Methods: This was a prospective cross-sectional study involving 400 singleton pregnancies between 19 and 40 weeks of gestation without any known risk factors of adverse pregnancy outcome who were referred for routine obstetric examination. The protocol included the doppler examination of fetal middle cerebral artery (MCA) within 2 mm after its origin from the internal carotid artery and data were used to construct the normograms and percentile fitted curves of each doppler parameter for different gestational age. Results: Among 400 singleton uncomplicated pregnancies between 19 and 40 weeks of gestation maximum number of pregnancies (10%) was at 19 weeks of gestation and minimum (2.5%) was at 31 weeks. The fetal peak systolic blood flow in the MCA showed significant correlation with period of gestation. Mean MCA-PSV was 22.35 ± 3.05 at 19 weeks of gestation which increased to 67.73 ± 9.92 at 40 weeks. The MCA-PSV showed continuous increment with increasing gestational age.Conclusions: Continuous increment in the peak systolic volume with advancing gestational age was obtained which was consistent with the previous studies done by various authors. The percentile fitted values and normograms will be valuable for the serial measurement of the peak systolic volume of the middle cerebral artery for complicated pregnancies.Nepal Journal of Obstetrics and Gynaecology / Vol 8 / No. 2 / Issue 16 / July-Dec, 2013 / 42-45 DOI: http://dx.doi.org/10.3126/njog.v8i2.9769  

scholarly journals ASSOCIATION OF SEROPOSITIVITY FOR CMV IGM AND IGG WITH ADVERSE PREGNANCY OUTCOME

2021 ◽  
Vol 71 (3) ◽  
pp. 831-35
Author(s):  
Ambreen Amna ◽  
Farkhunda Nadeem ◽  
Amin Fahim ◽  
Altaf Hussian Jatoi ◽  
Kanwal Abbas ◽  
...  
Keyword(s):  
Pregnancy Outcome ◽  
Adverse Pregnancy Outcome ◽  
Cross Sectional Study ◽  
Reproductive Age ◽  
University Hospital ◽  
Igg Antibodies ◽  
Cross Sectional ◽  
History Of ◽  
Adverse Pregnancy ◽  
Reproductive Age Group

Objective: To find out the prevalence of antibodies against cytomegalovirus and their association with adverse pregnancy outcome in women, at Isra University Hospital Hyderabad. Study Design: Cross-sectional study. Place and Duration of Study: Department of Gynaecology & Obstetrics, Isra University Hospital (IUH) Hyderabad, from Jan to Jun 2018. Methodology: Cytomegalovirus IgM and IgG antibodies in 305 women of reproductive age group were measured using the Enzyme Linked Fluorescent Assaysystem kit. Results: The combined positivity of anti cytomegalovirus IgG and IgM antibodies was 93 (30.40%). About 37 (37.37%) and 28 (34.14%) women were found to be seropositive for cytomegalovirus antibodies who had history of spontaneous miscarriage and recurrent miscarriages respectively. Conclusion: Higher association of seropositivity for cytomegalovirus IgM and IgG with bad obstetrical history and low economy was found.

scholarly journals Does Multiparity Affect Periodontitis Associated Adverse Pregnancy Outcomes' Awareness?

2021 ◽  
pp. 359-370
Author(s):  
Mohamed Abdelrasoul ◽  
Bashayer Bahamdain ◽  
Raghad Hasanain ◽  
Renad Barayan ◽  
Nada Bugis ◽  
...  
Keyword(s):  
Saudi Arabia ◽  
Pregnancy Outcomes ◽  
Adverse Pregnancy Outcome ◽  
Oral Bacteria ◽  
Adverse Pregnancy Outcomes ◽  
University Education ◽  
Cross Sectional ◽  
Convenience Sample ◽  
Validated Questionnaire ◽  
Adverse Pregnancy

Background: Periodontal disease is a very common, undesirable, and neglected bacterial infection causing destruction of the connective tissue and dental bone support. During pregnancy, the oral bacteria could lead to tissue damage and mediate immune response which can impair the development and fetal growth in the placenta that it may be a risk factor for pre-term birth (before 37 weeks of gestation). The goal of this study to measure the knowledge and awareness of women in Jeddah, Saudi Arabia toward the relation between periodontitis and adverse pregnancy outcome. Methodology: A cross-sectional study was done in Jeddah, Saudi Arabia from January 2020 until November 2021. based on a validated questionnaire developed by the authors. A convenience sample size of 966 women, aged 20-50 years, with a confidence level of 95%, and a 5% margin of error was selected. The questionnaire was divided into three main sections: demographics, knowledge and attitude. Results: The study showed a mean score of awareness of 3.801.26 (54.35 ± 17.98%) while the mean score of attitudes was 1.60 ± 0.98 (39.91 ± 24.42%). There was no statistically significant relationship to age group, nationality, or parity, however, scores were significant to university education level. Conclusion: Learning from previous multigravidas did not influence knowledge and awareness towards adverse pregnancy outcomes associated with PD.

scholarly journals Pregnancy outcome in obstetric cholestasis of pregnancy with active management

2018 ◽  
Vol 12 (2) ◽  
pp. 32-35
Author(s):  
Nira Singh Shrestha ◽  
Sajipta Panta
Keyword(s):  
Pregnancy Outcome ◽  
Perinatal Outcome ◽  
Adverse Pregnancy Outcome ◽  
Active Management ◽  
Hypertensive Disorder ◽  
Cross Sectional ◽  
The Mean ◽  
Adverse Pregnancy ◽  
Meconium Staining ◽  
Cholestasis Of Pregnancy

Aims: To study the maternal and perinatal outcome of pregnancies complicated with obstetric cholestasis with active management.Methods: This is a cross- sectional, descriptive study done at the department of obstetrics and gynecological of KMCTH for 24 months. All the cases of obstetric cholestasis that were managed by active management were enrolled as cases. Their demographic details, maternal and perinatal outcome were noted. Data was analyzed and presented as mean, percentage and frequency and presented as tables and figures. Results: Total 84 cases of obstetric cholestasis were managed by active management during the study period. The mean age of the women were 26.59 years (21-34 years), the mean gestational age at diagnosis was 32.53 weeks (18 - 38 weeks).  Diabetes mellitus was present in 17.85% and15.47% % had hypertensive disorder of pregnancy.All the cases had complaint of pruritus and 89.25% of the case had itching over abdomen, 73.78% of the women had itching over palms and soles. The itching was severe enough to cause sleep disturbance in over 65% of the cases. Meconium staining of liquor was present in 17.85% of the cases; cesarean section rate was 60.69%. There were 3 cases (3.57%) of postpartum hemorrhage but none required blood transfusion. There were no cases of still birth or neonatal death. Over 10% of the neonate had Apgar score less than 7 at 5 minute and approximately one fourth of the newborn required NICU care.Conclusions: Adverse pregnancy outcome associated with obstetric cholestasis can be minimized with active management of the cases.

scholarly journals The Challenge of Generating Causal Hypotheses Using Network Models

2019 ◽  
Author(s):  
Oisín Ryan ◽  
Laura Francina Bringmann ◽  
Noémi Katalin Schuurman
Keyword(s):  
Graphical Models ◽  
Markov Random Fields ◽  
Causal Structure ◽  
Network Models ◽  
R Package ◽  
Hypothesis Generation ◽  
Gaussian Graphical Models ◽  
Undirected Network ◽  
Path Models ◽  
The One

The network approach to psychopathology is a theoretical framework in which mental disorders are viewed as arising from direct causal interactions between symptoms. To investigate such networks, researchers typically estimate undirected network models from empirical data, called Pairwise Markov Random Fields (PMRFs), or for normally distributed variables, Gaussian Graphical Models (GGMs). In this paper, we critically evaluate the use of PMRF-based methods to generate causal hypotheses about an underlying directed causal structure. We argue that hypothesis generation is critically dependent on the specification of a target causal structure: This is generally absent from applications of PMRFs, researchers instead taking a causally-agnostic approach. We show that the agnostic approach is fundamentally problematic, since the heuristics typically used for hypothesis generation do not hold for all types of causal structure. We illustrate the difficulty of hypothesis generation in practice even under ideal conditions, that is, when a (weighted) Directed Acyclic Graph (DAG) is taken to be the target structure. Difficulties arise due to the one-to-many mapping from PMRF to causal structure: Many different DAGs can result in the same PMRF, and these differ in their substantive interpretations. We demonstrate this by re-analysing an empirical GGM analysis, aided by a novel tool (available for download as an R package) which allows us to map a given GGM onto an equivalence set of linear path models. We discuss additional barriers to discovering causal relationships in practice, possible alternative formalisms for causal structure, and ways forward for the field.

scholarly journals Relationship Between Placental Calcification and Estimated Fetal Weight Percentile at 30-34 Weeks of Pregnancy

2019 ◽  
Vol 7 (4) ◽  
pp. 478-482
Author(s):  
Mojgan Barati ◽  
Sara Masihi ◽  
Elnaz Barahimi ◽  
Mohammad Ali Khorrami
Keyword(s):  
Pregnant Women ◽  
Uterine Artery ◽  
Adverse Pregnancy Outcome ◽  
Multiple Pregnancy ◽  
Cross Sectional Study ◽  
Fetal Weight ◽  
Cross Sectional ◽  
Adverse Pregnancy ◽  
Doppler Indices ◽  
Estimated Fetal Weight

Objectives: The identification of at-risk fetus is considered as one of the most difficult challenges for clinicians and researchers although the clinical significance of placental calcifications (PCs) and its relation to adverse pregnancy outcome are controversial. Therefore, the present study aimed to evaluate the relationship between PC and estimated fetal weight (EFW) percentile at 30-34 weeks of pregnancy. Materials and Methods: This prospective cross-sectional study was carried out on all pregnant women except for multiple pregnancy subjects who were admitted to an outpatient perinatal center from October 2016 to September 2018. Several parameters were measured at 30-34 weeks of pregnancy, including EFW, umbilical artery pulsatility index (PI), middle cerebral artery PI, cerebroplacental ratio (CPR), right and left uterine artery PI, along with right and left uterine artery notch. Finally, the calcification of the placenta with any shape and degree was determined as well. Results: In this study, 739 pregnant women were evaluated for PC, including patients with PC (9.87%), small-for-gestational age (SGA, 3.65%), and those with at least one abnormal Doppler index (23.95%). Patients with PC and those with at least one abnormal Doppler index had significantly higher SGA (29.62% and 12.42%, respectively). In addition, there were 55.55% and 30.13% patients with SGA and PC in the group with at least one abnormality in terms of Doppler indices. Conclusions: In general, the findings showed that PC is more common in SGA. Based on the results, at least one abnormality in Doppler indices was more common in PC and SGA, and uterine artery Doppler abnormality was the most prevalent abnormal findings in the arterial Doppler. Thus, PC may be an important marker for adverse pregnancy outcomes.

scholarly journals A Stepwise Approach for High-Dimensional Gaussian Graphical Models

2021 ◽  
Vol 1 (2) ◽  
Author(s):  
Ruben Zamar ◽  
Marcelo Ruiz ◽  
Ginette Lafit ◽  
Javier Nogales
Keyword(s):  
Graphical Models ◽  
Partial Correlation ◽  
Pearson Correlation ◽  
Real Life ◽  
Correlation Coefficients ◽  
High Dimensional ◽  
Prediction Errors ◽  
Gaussian Graphical Models ◽  
Stepwise Approach ◽  
Linear Predictors

We present a stepwise approach to estimate high dimensional Gaussian graphical models. We exploit the relation between the partial correlation coefficients and the distribution of the prediction errors, and parametrize the model in terms of the Pearson correlation coefficients between the prediction errors of the nodes’ best linear predictors. We propose a novel stepwise algorithm for detecting pairs of conditionally dependent variables. We compare the proposed algorithm with existing methods including graphical lasso (Glasso), constrained `l1-minimization(CLIME) and equivalent partial correlation (EPC), via simulation studies and real life applications. In our simulation study we consider several model settings and report the results using different performance measures that look at desirable features of the recovered graph.

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