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Journal articles on the topic 'Bayes False discovery rate'

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Author: Grafiati

Published: 9 March 2023

Last updated: 11 March 2023

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1

Sarkar, Sanat K., and Tianhui Zhou. "Controlling Bayes directional false discovery rate in random effects model." Journal of Statistical Planning and Inference 138, no. 3 (March 2008): 682–93. http://dx.doi.org/10.1016/j.jspi.2007.01.006.

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2

Hollister, Megan C., and Jeffrey D. Blume. "4497 Accessible False Discovery Rate Computation." Journal of Clinical and Translational Science 4, s1 (June 2020): 44. http://dx.doi.org/10.1017/cts.2020.164.

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OBJECTIVES/GOALS: To improve the implementation of FDRs in translation research. Current statistical packages are hard to use and fail to adequately convey strong assumptions. We developed a software package that allows the user to decide on assumptions and choose the hey desire. We encourage wider reporting of FDRs for observed findings. METHODS/STUDY POPULATION: We developed a user-friendly R function for computing FDRs from observed p-values. A variety of methods for FDR estimation and for FDR control are included so the user can select the approach most appropriate for their setting. Options include Efron’s Empirical Bayes FDR, Benjamini-Hochberg FDR control for multiple testing, Lindsey’s method for smoothing empirical distributions, estimation of the mixing proportion, and central matching. We illustrate the important difference between estimating the FDR for a particular finding and adjusting a hypothesis test to control the false discovery propensity. RESULTS/ANTICIPATED RESULTS: We performed a comparison of the capabilities of our new p.fdr function to the popular p.adjust function from the base stats-package. Specifically, we examined multiple examples of data coming from different unknown mixture distributions to highlight the null estimation methods p.fdr includes. The base package does not provide the optimal FDR usage nor sufficient estimation options. We also compared the step-up/step-down procedure used in adjusted p-value hypothesis test and discuss when this is inappropriate. The p.adjust function is not able to report raw-adjusted values and this will be shown in the graphical results. DISCUSSION/SIGNIFICANCE OF IMPACT: FDRs reveal the propensity for an observed result to be incorrect. FDRs should accompany observed results to help contextualize the relevance and potential impact of research findings. Our results show that previous methods are not sufficient rich or precise in their calculations. Our new package allows the user to be in control of the null estimation and step-up implementation when reporting FDRs.

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3

Muralidharan, Omkar. "An empirical Bayes mixture method for effect size and false discovery rate estimation." Annals of Applied Statistics 4, no. 1 (March 2010): 422–38. http://dx.doi.org/10.1214/09-aoas276.

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4

SHRINER, DANIEL. "Mapping multiple quantitative trait loci under Bayes error control." Genetics Research 91, no. 3 (June 2009): 147–59. http://dx.doi.org/10.1017/s001667230900010x.

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SummaryIn mapping of quantitative trait loci (QTLs), performing hypothesis tests of linkage to a phenotype of interest across an entire genome involves multiple comparisons. Furthermore, linkage among loci induces correlation among tests. Under many multiple comparison frameworks, these problems are exacerbated when mapping multiple QTLs. Traditionally, significance thresholds have been subjectively set to control the probability of detecting at least one false positive outcome, although such thresholds are known to result in excessively low power to detect true positive outcomes. Recently, false discovery rate (FDR)-controlling procedures have been developed that yield more power both by relaxing the stringency of the significance threshold and by retaining more power for a given significance threshold. However, these procedures have been shown to perform poorly for mapping QTLs, principally because they ignore recombination fractions between markers. Here, I describe a procedure that accounts for recombination fractions and extends FDR control to include simultaneous control of the false non-discovery rate, i.e. the overall error rate is controlled. This procedure is developed in the Bayesian framework using a direct posterior probability approach. Data-driven significance thresholds are determined by minimizing the expected loss. The procedure is equivalent to jointly maximizing positive and negative predictive values. In the context of mapping QTLs for experimental crosses, the procedure is applicable to mapping main effects, gene–gene interactions and gene–environment interactions.

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Amar, David, Ron Shamir, and Daniel Yekutieli. "Extracting replicable associations across multiple studies: Empirical Bayes algorithms for controlling the false discovery rate." PLOS Computational Biology 13, no. 8 (August 18, 2017): e1005700. http://dx.doi.org/10.1371/journal.pcbi.1005700.

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6

Noma, Hisashi, and Shigeyuki Matsui. "An Empirical Bayes Optimal Discovery Procedure Based on Semiparametric Hierarchical Mixture Models." Computational and Mathematical Methods in Medicine 2013 (2013): 1–9. http://dx.doi.org/10.1155/2013/568480.

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Multiple testing has been widely adopted for genome-wide studies such as microarray experiments. For effective gene selection in these genome-wide studies, the optimal discovery procedure (ODP), which maximizes the number of expected true positives for each fixed number of expected false positives, was developed as a multiple testing extension of the most powerful test for a single hypothesis by Storey (Journal of the Royal Statistical Society, Series B,vol. 69, no. 3, pp. 347–368, 2007). In this paper, we develop an empirical Bayes method for implementing the ODP based on a semiparametric hierarchical mixture model using the “smoothing-by-roughening" approach. Under the semiparametric hierarchical mixture model, (i) the prior distribution can be modeled flexibly, (ii) the ODP test statistic and the posterior distribution are analytically tractable, and (iii) computations are easy to implement. In addition, we provide a significance rule based on the false discovery rate (FDR) in the empirical Bayes framework. Applications to two clinical studies are presented.

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7

Quatto, Piero, Nicolò Margaritella, Isa Costantini, Francesca Baglio, Massimo Garegnani, Raffaello Nemni, and Luigi Pugnetti. "Brain networks construction using Bayes FDR and average power function." Statistical Methods in Medical Research 29, no. 3 (May 14, 2019): 866–78. http://dx.doi.org/10.1177/0962280219844288.

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Brain functional connectivity is a widely investigated topic in neuroscience. In recent years, the study of brain connectivity has been largely aided by graph theory. The link between time series recorded at multiple locations in the brain and the construction of a graph is usually an adjacency matrix. The latter converts a measure of the connectivity between two time series, typically a correlation coefficient, into a binary choice on whether the two brain locations are functionally connected or not. As a result, the choice of a threshold τ over the correlation coefficient is key. In the present work, we propose a multiple testing approach to the choice of τ that uses the Bayes false discovery rate and a new estimator of the statistical power called average power function to balance the two types of statistical error. We show that the proposed average power function estimator behaves well both in case of independence and weak dependence of the tests and it is reliable under several simulated dependence conditions. Moreover, we propose a robust method for the choice of τ using the 5% and 95% percentiles of the average power function and False Discovery Rate bootstrap distributions, respectively, to improve stability. We applied our approach to functional magnetic resonance imaging and high density electroencephalogram data.

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8

Yang, Zhenyu, Zuojing Li, and David R. Bickel. "Empirical Bayes estimation of posterior probabilities of enrichment: A comparative study of five estimators of the local false discovery rate." BMC Bioinformatics 14, no. 1 (2013): 87. http://dx.doi.org/10.1186/1471-2105-14-87.

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9

You, Na, and Xueqin Wang. "An empirical Bayes method for robust variance estimation in detecting DEGs using microarray data." Journal of Bioinformatics and Computational Biology 15, no. 05 (October 2017): 1750020. http://dx.doi.org/10.1142/s0219720017500202.

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The microarray technology is widely used to identify the differentially expressed genes due to its high throughput capability. The number of replicated microarray chips in each group is usually not abundant. It is an efficient way to borrow information across different genes to improve the parameter estimation which suffers from the limited sample size. In this paper, we use a hierarchical model to describe the dispersion of gene expression profiles and model the variance through the gene expression level via a link function. A heuristic algorithm is proposed to estimate the hyper-parameters and link function. The differentially expressed genes are identified using a multiple testing procedure. Compared to SAM and LIMMA, our proposed method shows a significant superiority in term of detection power as the false discovery rate being controlled.

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10

Tabash, Mohammed, Mohamed Abd Allah, and Bella Tawfik. "Intrusion Detection Model Using Naive Bayes and Deep Learning Technique." International Arab Journal of Information Technology 17, no. 2 (February 28, 2019): 215–24. http://dx.doi.org/10.34028/iajit/17/2/9.

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The increase of security threats and hacking the computer networks are one of the most dangerous issues should treat in these days. Intrusion Detection Systems (IDSs), are the most appropriate methods to prevent and detect the attacks of networks and computer systems. This study presents several techniques to discover network anomalies using data mining tasks, Machine learning technology and dependence of artificial intelligence techniques. In this research, the smart hybrid model was developed to explore any penetrations inside the network. The model divides into two basic stages. The first stage includes the Genetic Algorithm (GA) in selecting the characteristics with depends on a process of extracting, Discretize And dimensionality reduction through Proportional K-Interval Discretization (PKID) and Fisher Linear Discriminant Analysis (FLDA) on respectively. At the end of the first stage combining Naïve Bayes classifier (NB) and Decision Table (DT) using NSL-KDD data set divided into two separate groups for training and testing. The second stage completely depends on the first stage outputs (predicted class) and reclassified with multilayer perceptrons using Deep Learning4J (DL) and the use of algorithm Stochastic Gradient Descent (SGD). In order to improve the performance in terms of the accuracy in classification of penetrations, raising the average of discovering and reducing the false alarms. The comparison of the proposed model and conventional models show the superiority of the proposed model and the previous conventional hybrid models. The result of the proposed model is 99.9325 of classification accuracy, the rate of detection is 99.9738 and 0.00093 of false alarms

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11

Sarsani, Vishal, Berent Aldikacti, Shai He, Rilee Zeinert, Peter Chien, and Patrick Flaherty. "Model-based identification of conditionally-essential genes from transposon-insertion sequencing data." PLOS Computational Biology 18, no. 3 (March 7, 2022): e1009273. http://dx.doi.org/10.1371/journal.pcbi.1009273.

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The understanding of bacterial gene function has been greatly enhanced by recent advancements in the deep sequencing of microbial genomes. Transposon insertion sequencing methods combines next-generation sequencing techniques with transposon mutagenesis for the exploration of the essentiality of genes under different environmental conditions. We propose a model-based method that uses regularized negative binomial regression to estimate the change in transposon insertions attributable to gene-environment changes in this genetic interaction study without transformations or uniform normalization. An empirical Bayes model for estimating the local false discovery rate combines unique and total count information to test for genes that show a statistically significant change in transposon counts. When applied to RB-TnSeq (randomized barcode transposon sequencing) and Tn-seq (transposon sequencing) libraries made in strains of Caulobacter crescentus using both total and unique count data the model was able to identify a set of conditionally beneficial or conditionally detrimental genes for each target condition that shed light on their functions and roles during various stress conditions.

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12

Dudoit, Sandrine, Houston N. Gilbert, and Mark J. van der Laan. "Resampling-Based Empirical Bayes Multiple Testing Procedures for Controlling Generalized Tail Probability and Expected Value Error Rates: Focus on the False Discovery Rate and Simulation Study." Biometrical Journal 50, no. 5 (October 2008): 716–44. http://dx.doi.org/10.1002/bimj.200710473.

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13

GUO, XU, and WEI PAN. "USING WEIGHTED PERMUTATION SCORES TO DETECT DIFFERENTIAL GENE EXPRESSION WITH MICROARRAY DATA." Journal of Bioinformatics and Computational Biology 03, no. 04 (August 2005): 989–1006. http://dx.doi.org/10.1142/s021972000500134x.

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A class of nonparametric statistical methods, including a nonparametric empirical Bayes (EB) method, the Significance Analysis of Microarrays (SAM) and the mixture model method (MMM) have been proposed to detect differential gene expression for replicated microarray experiments. They all depend on constructing a test statistic, for example, a t-statistic, and then using permutation to draw inferences. However, due to special features of microarray data, using standard permutation scores may not estimate the null distribution of the test statistic well, leading to possibly too conservative inferences. We propose a new method of constructing weighted permutation scores to overcome the problem: posterior probabilities of having no differential expression from the EB method are used as weights for genes to better estimate the null distribution of the test statistic. We also propose a weighted method to estimate the false discovery rate (FDR) using the posterior probabilities. Using simulated data and real data for time-course microarray experiments, we show the improved performance of the proposed methods when implemented in MMM, EB and SAM.

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Andrade, Daniel, and Yuzuru Okajima. "Adaptive covariate acquisition for minimizing total cost of classification." Machine Learning 110, no. 5 (April 18, 2021): 1067–104. http://dx.doi.org/10.1007/s10994-021-05958-z.

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AbstractIn some applications, acquiring covariates comes at a cost which is not negligible. For example in the medical domain, in order to classify whether a patient has diabetes or not, measuring glucose tolerance can be expensive. Assuming that the cost of each covariate, and the cost of misclassification can be specified by the user, our goal is to minimize the (expected) total cost of classification, i.e. the cost of misclassification plus the cost of the acquired covariates. We formalize this optimization goal using the (conditional) Bayes risk and describe the optimal solution using a recursive procedure. Since the procedure is computationally infeasible, we consequently introduce two assumptions: (1) the optimal classifier can be represented by a generalized additive model, (2) the optimal sets of covariates are limited to a sequence of sets of increasing size. We show that under these two assumptions, a computationally efficient solution exists. Furthermore, on several medical datasets, we show that the proposed method achieves in most situations the lowest total costs when compared to various previous methods. Finally, we weaken the requirement on the user to specify all misclassification costs by allowing the user to specify the minimally acceptable recall (target recall). Our experiments confirm that the proposed method achieves the target recall while minimizing the false discovery rate and the covariate acquisition costs better than previous methods.

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15

Xie, Yang, Kyeong S. Jeong, Wei Pan, Arkady Khodursky, and Bradley P. Carlin. "A Case Study on Choosing Normalization Methods and Test Statistics for Two-Channel Microarray Data." Comparative and Functional Genomics 5, no. 5 (2004): 432–44. http://dx.doi.org/10.1002/cfg.416.

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DNA microarray analysis is a biological technology which permits the whole genome to be monitored simultaneously on a single slide. Microarray technology not only opens an exciting research area for biologists, but also provides significant new challenges to statisticians. Two very common questions in the analysis of microarray data are, first, should we normalize arrays to remove potential systematic biases, and if so, what normalization method should we use? Second, how should we then implement tests of statistical significance? Straightforward and uniform answers to these questions remain elusive. In this paper, we use a real data example to illustrate a practical approach to addressing these questions. Our data is taken from a DNA–protein binding microarray experiment aimed at furthering our understanding of transcription regulation mechanisms, one of the most important issues in biology. For the purpose of preprocessing data, we suggest looking at descriptive plots first to decide whether we need preliminary normalization and, if so, how this should be accomplished. For subsequent comparative inference, we recommend use of an empirical Bayes method (theBstatistic), since it performs much better than traditional methods, such as the sample mean (Mstatistic) and Student'ststatistic, and it is also relatively easy to compute and explain compared to the others. The false discovery rate (FDR) is used to evaluate the different methods, and our comparative results lend support to our above suggestions.

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16

Bickel, David R. "Confidence distributions applied to propagating uncertainty to inference based on estimating the local false discovery rate: A fiducial continuum from confidence sets to empirical Bayes set estimates as the number of comparisons increases." Communications in Statistics - Theory and Methods 46, no. 21 (August 2, 2017): 10788–99. http://dx.doi.org/10.1080/03610926.2016.1248781.

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17

Gonzalez, Velda J., Farnoosh Abbas-Aghababazadeh, Brooke L. Fridley, Tomar Ghansah, and Leorey N. Saligan. "Expression of Sestrin Genes in Radiotherapy for Prostate Cancer and Its Association With Fatigue: A Proof-of-Concept Study." Biological Research For Nursing 20, no. 2 (January 11, 2018): 218–26. http://dx.doi.org/10.1177/1099800417749319.

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Genetic factors that influence inflammation and energy production/expenditure in cells may affect patient outcomes following treatment with external beam radiation therapy (EBRT). Sestrins, stress-inducible genes with antioxidant properties, have recently been implicated in several behaviors including fatigue. This proof-of-concept study explored whether the sestrin family of genes ( SESN1, SESN2, and SESN3) were differentially expressed from baseline to the midpoint of EBRT in a sample of 26 Puerto Rican men with nonmetastatic prostate cancer. We also examined whether changes in expression of these genes were associated with changes in fatigue scores during EBRT. Method: Participants completed the 13-item Functional Assessment of Cancer Therapy—Fatigue subscale, Spanish version. Whole blood samples were collected at baseline and at the midpoint of EBRT. Gene expression data were analyzed using the limma package in the R (version R 2.14.0.) statistical software. Linear models and empirical Bayes moderation, adjusted for radiation fraction (total number of days of prescribed radiation treatment), were used to examine potential associations between changes in gene expression and change in fatigue scores. Results: Expression of SESN3 (adjusted p < .01, log fold change −0.649) was significantly downregulated during EBRT, whereas the expressions of SESN1 and SESN2 remained unchanged. After adjustment for radiation fraction, change in SESN3 expression was associated with change in fatigue during EBRT (false discovery rate <.01). Conclusions: Downregulation of SESN3, a novel pharmacoactive stress response gene, was associated with fatigue intensification during EBRT. SESN3 may serve as an interventional target and a biomarker for the cellular and molecular events associated with EBRT-related fatigue.

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Saha, Sunil, Jagabandhu Roy, Alireza Arabameri, Thomas Blaschke, and Dieu Tien Bui. "Machine Learning-Based Gully Erosion Susceptibility Mapping: A Case Study of Eastern India." Sensors 20, no. 5 (February 28, 2020): 1313. http://dx.doi.org/10.3390/s20051313.

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Gully erosion is a form of natural disaster and one of the land loss mechanisms causing severe problems worldwide. This study aims to delineate the areas with the most severe gully erosion susceptibility (GES) using the machine learning techniques Random Forest (RF), Gradient Boosted Regression Tree (GBRT), Naïve Bayes Tree (NBT), and Tree Ensemble (TE). The gully inventory map (GIM) consists of 120 gullies. Of the 120 gullies, 84 gullies (70%) were used for training and 36 gullies (30%) were used to validate the models. Fourteen gully conditioning factors (GCFs) were used for GES modeling and the relationships between the GCFs and gully erosion was assessed using the weight-of-evidence (WofE) model. The GES maps were prepared using RF, GBRT, NBT, and TE and were validated using area under the receiver operating characteristic (AUROC) curve, the seed cell area index (SCAI) and five statistical measures including precision (PPV), false discovery rate (FDR), accuracy, mean absolute error (MAE), and root mean squared error (RMSE). Nearly 7% of the basin has high to very high susceptibility for gully erosion. Validation results proved the excellent ability of these models to predict the GES. Of the analyzed models, the RF (AUROC = 0.96, PPV = 1.00, FDR = 0.00, accuracy = 0.87, MAE = 0.11, RMSE = 0.19 for validation dataset) is accurate enough for modeling and better suited for GES modeling than the other models. Therefore, the RF model can be used to model the GES areas not only in this river basin but also in other areas with the same geo-environmental conditions.

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Mallik, Saurav, Soumita Seth, Tapas Bhadra, and Zhongming Zhao. "A Linear Regression and Deep Learning Approach for Detecting Reliable Genetic Alterations in Cancer Using DNA Methylation and Gene Expression Data." Genes 11, no. 8 (August 12, 2020): 931. http://dx.doi.org/10.3390/genes11080931.

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DNA methylation change has been useful for cancer biomarker discovery, classification, and potential treatment development. So far, existing methods use either differentially methylated CpG sites or combined CpG sites, namely differentially methylated regions, that can be mapped to genes. However, such methylation signal mapping has limitations. To address these limitations, in this study, we introduced a combinatorial framework using linear regression, differential expression, deep learning method for accurate biological interpretation of DNA methylation through integrating DNA methylation data and corresponding TCGA gene expression data. We demonstrated it for uterine cervical cancer. First, we pre-filtered outliers from the data set and then determined the predicted gene expression value from the pre-filtered methylation data through linear regression. We identified differentially expressed genes (DEGs) by Empirical Bayes test using Limma. Then we applied a deep learning method, “nnet” to classify the cervical cancer label of those DEGs to determine all classification metrics including accuracy and area under curve (AUC) through 10-fold cross validation. We applied our approach to uterine cervical cancer DNA methylation dataset (NCBI accession ID: GSE30760, 27,578 features covering 63 tumor and 152 matched normal samples). After linear regression and differential expression analysis, we obtained 6287 DEGs with false discovery rate (FDR) <0.001. After performing deep learning analysis, we obtained average classification accuracy 90.69% (±1.97%) of the uterine cervical cancerous labels. This performance is better than that of other peer methods. We performed in-degree and out-degree hub gene network analysis using Cytoscape. We reported five top in-degree genes (PAIP2, GRWD1, VPS4B, CRADD and LLPH) and five top out-degree genes (MRPL35, FAM177A1, STAT4, ASPSCR1 and FABP7). After that, we performed KEGG pathway and Gene Ontology enrichment analysis of DEGs using tool WebGestalt(WEB-based Gene SeT AnaLysis Toolkit). In summary, our proposed framework that integrated linear regression, differential expression, deep learning provides a robust approach to better interpret DNA methylation analysis and gene expression data in disease study.

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Crager, M. R., and S. Shak. "Are more genes better in clinical-genomic studies? A mathematical model to define identification power for clinically relevant genes." Journal of Clinical Oncology 27, no. 15_suppl (May 20, 2009): e22136-e22136. http://dx.doi.org/10.1200/jco.2009.27.15_suppl.e22136.

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e22136 Background: Modern molecular technologies that drive personalized medicine can generate expression data for thousands of candidate genes, or indeed, data for the “whole genome”. Clinical-genomic studies aim to identify genes that are truly associated with clinical outcome. We investigated the impact of large numbers of genes with little or no association with clinical outcome on the statistical power of studies to identify individual genes with strong association. Methods: We adapted Efron's (Ann Stat 2007) empirical Bayes approach to develop a method to calculate the identification power for individual genes in analyses that control the false discovery rate (FDR), the expected proportion of false positives. The identification power is the probability that a gene with a given true magnitude of association with clinical outcome will be identified when we control the FDR at a specified level. The identification power also depends on the proportion of genes studied that are not associated with outcome and the distribution of the degree of association among genes that are. Results: The identification power for clinically relevant genes decreases dramatically as the proportion of genes having no association with clinical outcome increases. For example, in a scenario in which 100 genes have some association with clinical outcome [median hazard ratio (HR) 1.125], increasing the number of genes having no association from 400 to 4000 decreases the identification power an individual gene having strong association (HR 1.42) from 80% to 36%. Similarly, when the number of genes having no association is 400 and the median HR among the 100 genes that have an association is decreased from 1.125 to 1.06, identification power for a gene with an association HR of 1.42 decreases from 80% to 62%. Conclusions: Identification power can be used to optimize strategies for gene finding and enable personalized medicine. Although technology allows the assay of ever-increasing numbers of genes, inclusion in a single study of many genes unrelated to clinical outcome is detrimental to the identification power for clinically relevant genes. Even with increased power, replication of results in independent studies will continue to be critically important. [Table: see text]

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Walejko, Jacquelyn M., Jeremy P. Koelmel, Timothy J. Garrett, Arthur S. Edison, and Maureen Keller-Wood. "Multiomics approach reveals metabolic changes in the heart at birth." American Journal of Physiology-Endocrinology and Metabolism 315, no. 6 (December 1, 2018): E1212—E1223. http://dx.doi.org/10.1152/ajpendo.00297.2018.

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During late gestation, the fetal heart primarily relies on glucose and lactate to support rapid growth and development. Although numerous studies describe changes in heart metabolism to utilize fatty acids preferentially a few weeks after birth, little is known about metabolic changes of the heart within the first day following birth. Therefore, we used the ovine model of pregnancy to investigate metabolic differences between the near-term fetal and the newborn heart. Heart tissue was collected for metabolomic, lipidomic, and transcriptomic approaches from the left and right ventricles and intraventricular septum in 7 fetuses at gestational day 142 and 7 newborn lambs on the day of birth. Significant metabolites and lipids were identified using a Student’s t-test, whereas differentially expressed genes were identified using a moderated t-test with empirical Bayes method [false discovery rate (FDR)-corrected P < 0.10]. Single-sample gene set enrichment analysis (ssGSEA) was used to identify pathways enriched on a transcriptomic level (FDR-corrected P < 0.05), whereas overrepresentation enrichment analysis was used to identify pathways enriched on a metabolomic level ( P < 0.05). We observed greater abundance of metabolites involved in butanoate and propanoate metabolism, and glycolysis in the term fetal heart and differential expression in these pathways were confirmed with ssGSEA. Immediately following birth, newborn hearts displayed enrichment in purine, fatty acid, and glycerophospholipid metabolic pathways as well as oxidative phosphorylation with significant alterations in both lipids and metabolites to support transcriptomic findings. A better understanding of metabolic alterations that occur in the heart following birth may improve treatment of neonates at risk for heart failure.

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Kim, Jenny J., Mariette Labots, Luigi Marchionni, Shahnaz Begum, Gerrit A. Meijer, Henk M. W. Verheul, Michael Anthony Carducci, et al. "Genome-wide methylation profiling to identify potential epigenetic biomarkers associated with response to sunitinib in metastatic renal cell cancer (mRCC) patients (pts)." Journal of Clinical Oncology 31, no. 15_suppl (May 20, 2013): 4566. http://dx.doi.org/10.1200/jco.2013.31.15_suppl.4566.

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4566 Background: There exists a significant heterogeneity in the clinical response to sunitinib among pts treated for mRCC and, thus, a biomarker which would predict pts’ response up front would be an invaluable tool in the clinical management of these pts. In this regard, whole genome methylation array was performed between 2 extreme groups: sunitinib responders (RES) and non-responders (NRES) to identify differentially methylated genes between these subsets of pts. Methods: mRCC pts who received sunitinib therapy with available frozen nephrectomy tissues (stored at -80°C) and clinical data were identified. RES were identified as pts with progression free survival (PFS) of > or =11 months (mos) and NRES as those with PFS < or = 3 mos. After DNA extraction and quality assurance according to standard protocol, whole genome methylation array was performed using Infinium Humanmethylation450 BeadChip Kit - Illumina. Data normalization was achieved by subset-quantile within array normalization (PMID: 22703947). Differentially methylated regions were identified using logit transformed Beta values, using an F-test after shrinking variance via empirical Bayes (PMID: 21118553). Results: Total of 13 pts who received sunitinib therapy with available frozen nephrectomy tissues were identified. Of the 13, 5 pts qualified for each RES and NRES cohort as described in methods section. All pts in RES group had clear cell subtype. Two pts of the NRES group were of non-clear cell subtype. The QC plots showed that all arrays were successful. For RES vs. NRES, one genomic location, DENND2D, was significantly hypermethylated in the RES group with a false discovery rate (FDR) of <10%. DENND2D has been identified as a tumor suppressor-like gene in non-small cell lung cancer and melanoma cell lines in the recent past. Conclusions: In this study, DENND2D was significantly hypermethylated in sunitinib RES compared to NRES among mRCC pts. Data analysis with a less stringent FDR is also being pursued. Technical validation as well as clinical validation of DENND2D utilizing a larger pt cohort are ongoing.

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Guturu, Harendra, Andrew C. Doxey, Aaron M. Wenger, and Gill Bejerano. "Structure-aided prediction of mammalian transcription factor complexes in conserved non-coding elements." Philosophical Transactions of the Royal Society B: Biological Sciences 368, no. 1632 (December 19, 2013): 20130029. http://dx.doi.org/10.1098/rstb.2013.0029.

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Mapping the DNA-binding preferences of transcription factor (TF) complexes is critical for deciphering the functions of cis -regulatory elements. Here, we developed a computational method that compares co-occurring motif spacings in conserved versus unconserved regions of the human genome to detect evolutionarily constrained binding sites of rigid TF complexes. Structural data were used to estimate TF complex physical plausibility, explore overlapping motif arrangements seldom tackled by non-structure-aware methods, and generate and analyse three-dimensional models of the predicted complexes bound to DNA. Using this approach, we predicted 422 physically realistic TF complex motifs at 18% false discovery rate, the majority of which (326, 77%) contain some sequence overlap between binding sites. The set of mostly novel complexes is enriched in known composite motifs, predictive of binding site configurations in TF–TF–DNA crystal structures, and supported by ChIP-seq datasets. Structural modelling revealed three cooperativity mechanisms: direct protein–protein interactions, potentially indirect interactions and ‘through-DNA’ interactions. Indeed, 38% of the predicted complexes were found to contain four or more bases in which TF pairs appear to synergize through overlapping binding to the same DNA base pairs in opposite grooves or strands. Our TF complex and associated binding site predictions are available as a web resource at http://bejerano.stanford.edu/complex .

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So, Hon-Cheong, Kwan-Long Chau, Fu-Kiu Ao, Cheuk-Hei Mo, and Pak-Chung Sham. "Exploring shared genetic bases and causal relationships of schizophrenia and bipolar disorder with 28 cardiovascular and metabolic traits." Psychological Medicine 49, no. 08 (July 26, 2018): 1286–98. http://dx.doi.org/10.1017/s0033291718001812.

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AbstractBackgroundCardiovascular diseases represent a major health issue in patients with schizophrenia (SCZ) and bipolar disorder (BD), but the exact nature of cardiometabolic (CM) abnormalities involved and the underlying mechanisms remain unclear. Psychiatric medications are known risk factors, but it is unclear whether there is a connection between the disorders (SCZ/BD) themselves and CM abnormalities.MethodsUsing polygenic risk scores and linkage disequilibrium score regression, we investigated the shared genetic bases of SCZ and BD with 28 CM traits. We performed Mendelian randomization (MR) to elucidate causal relationships between the two groups of disorders. The analysis was based on large-scale meta-analyses of genome-wide association studies. We also identified the potential shared genetic variants and inferred the pathways involved.ResultsWe found tentative polygenic associations of SCZ with glucose metabolism abnormalities, adverse adipokine profiles, increased waist-to-hip ratio and visceral adiposity (false discovery rate or FDR<0.05). However, there was an inverse association with body mass index. For BD, we observed several polygenic associations with favorable CM profiles at FDR<0.05. MR analysis showed that SCZ may be causally linked to raised triglyceride and that lower fasting glucose may be linked to BD. We also identified numerous single nucleotide polymorphisms and pathways shared between SCZ/BD with CM traits, some of which are related to inflammation or the immune system.ConclusionsOur findings suggest that SCZ patients may be genetically predisposed to several CM abnormalities independent of medication side effects. On the other hand, CM abnormalities in BD may be more likely to be secondary. However, the findings require further validation.

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Zhao, Zhigen, and J. T. Gene Hwang. "Empirical Bayes false coverage rate controlling confidence intervals." Journal of the Royal Statistical Society: Series B (Statistical Methodology) 74, no. 5 (June 21, 2012): 871–91. http://dx.doi.org/10.1111/j.1467-9868.2012.01033.x.

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Sebastiani, Paola, Jacqueline N. Milton, Nadia Timofeev, Stephen W. Hartley, Daniel A. Dworkis, Efthymia Melista, Clinton T. Baldwin, and Martin H. Steinberg. "Genome-Wide Association Study of Stroke in Sickle Cell Anemia." Blood 114, no. 22 (November 20, 2009): 1528. http://dx.doi.org/10.1182/blood.v114.22.1528.1528.

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Abstract Abstract 1528 Poster Board I-551 Stroke is a potentially lethal complication of sickle cell anemia (SCA) and one marker of sickle vasculopathy. Candidate gene studies conducted have demonstrated that stroke is associated with polymorphisms (SNPs) in several genes whose interactions can be used to build risk prediction models. For an unbiased discovery of the complex genetic basis of this complication, we conducted a genome-wide association study in 1387 SCA patients from the Cooperative Study of Sickle Cell Disease to identify single nucleotide polymorphisms (SNPs) associated with stroke. The data included 145 patients with at least one stroke event (cases), and 1242 stroke free patients (controls). Cases and controls had approximately the same median age (18 years) and similar gender composition (cases: 56% males; controls: 52% males). DNA was genotyped with the IIlumina Human610-Quad that includes approximately 600,000 SNPs and we removed samples with a call rate < 93%, and samples with a mismatch between gender reported in the database and heterozygosity of more than 5% SNPs in chromosome X. Error rate was estimated to be less than 5% based on agreement between known repeated samples and identical samples matched using genome-wide identity by descent using the software PLINK. We examined general, allelic, dominant and recessive associations of each individual SNP using Bayesian tests and scored the evidence of association of each model by its posterior probability. We assumed uniform probability on competitive models, so that the posterior odds of each model of association relative to the model of no association is equivalent to the Bayes factor (BF) and conducted extensive simulations to compute the expected number of false positive associations for different thresholds of the BF. The simulations showed that the false positive rate of the Bayesian decision rule changes with the allele frequency and suggested using a BF > 10,000 to reduce the expected number of false positive associations to less than 1 in 100,000 independent tests. Twenty-six SNPs passed this threshold, 15 SNPs were in intragenic regions and 10 SNPs were in known genes, including one SNP in the brain specific angiogenesis inhibitor BAI1 (rs11167147, odds for stroke in carriers of the AC or CC genotype = 0.25 relative to carriers of the AA genotype, BF>22,000) and one SNP in the regulator of angiogenes AIMP1 (rs7654865: odds for stroke in carriers of the AC or CC genotype = 0.10 relative relative to carriers of he AA genotype, BF>10,000). SNPs in other genes involved in angiogenesis (ANGPT1, ANGPT4 and TEK) were also associated with stroke, although none of the associations reached genome-wide significance. The regulation of angiogenesis is controlled by a balance between stimulators and inhibitors. BAI1 is a p53 target gene specifically expressed in the brain that is a transmembrane protein containing an extracellular domain with thrombospondin type-1 repeats that can exhibit anti-angiogenic activity. BAI1 is a mediator in the p53-signaling pathway; p53 has been shown to result in the decreased expression of VEGF and increased expression of BAI1. The VEGF system is integrated into the p53 transcriptional network and both pathways can be abnormal in SCA vessels. AIMP1 encodes a cytokine induced by apoptosis that is involved in the control of angiogenesis, inflammation and wound healing. It induces the expression of TNFRSF1A in endothelial cells and has anti-angiogenic functions via inhibition of HIF1α activities. HIF1α is involved in mediating angiogenic growth of endothelial cells. None of the SNPs in genes that we found associated with stroke in earlier studies reached genome-wide significance, although several SNPs in BMP6, ADCY9, EDN1, ERG, MET, SELP, TEK and TGFBR3 reached lesser statistical significance. We also looked for replication of SNPs that have been associated with stroke in the general population; rs12229103 (NINJ2) was significantly associated with stroke (BF>10). This SNP is within 20kb from rs12425791 that was found to be associated with stroke in the general population. Also SNPs in IMPA2 and AIM1 were significantly associated. Although confirmation of our genetic studies in an independent sample of individuals is needed and functional studies are warranted, our findings provide suggestive evidence for a major role of genes involved in angiogenesis in the modulation of stroke risk, a finding is in agreement with previous work suggesting that angiogenesis is dysregulated in SCA. Disclosures No relevant conflicts of interest to declare.

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Payton, R. R., L. A. Rispoli, and J. L. Edwards. "193 DIFFERENTIAL GENE EXPRESSION IN CUMULUS CELLS OF DEVELOPMENTALLY COMPETENT V. CHALLENGED BOVINE OOCYTES." Reproduction, Fertility and Development 21, no. 1 (2009): 195. http://dx.doi.org/10.1071/rdv21n1ab193.

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It is well established that exposure of cumulus–oocyte complexes (COC) to heat stress during the first 12 h of maturation reduces blastocyst development by 42 to 65%. Previous research supports the notion that some of the effects of heat stress on oocyte competence may be cumulus-mediated. To determine the extent to which this may occur, COC were matured at 38.5°C for 24 h (control) or 41°C for the first 12 h of maturation followed by 38.5°C for remaining 12 h (heat stress). A subset of COC underwent IVF with Percoll-prepared sperm and then was cultured in KSOM containing 0.5% BSA to assess developmental competence. Remaining oocytes were denuded. Cumulus cells, kept separate by treatment, were stored in lysis buffer at –80°C until RNA extraction. Total RNA from cumulus was amplified prior to hybridization to bovine Affymetrix GeneChips (Affymetrix Inc., Santa Clara, CA, USA; n = 8 pools per treatment collected on 8 different occasions; n = 16 chips). Following pre-processing using the MAS5.0 algorithm, microarray data were subjected to linear modeling and empirical Bayes analyses (Bioconductor, Limma package). False discovery rate was controlled using the Benjamini and Hochberg method, and differentially expressed genes were selected by an adjusted P-value (P < 0.05). Functional annotation of selected genes was performed using NetAffx (Affymetrix Inc.) and Database for Annotation, Visualization and Integrated Discovery (DAVID; NIAID, NIH, Bethesda, MD, USA). Heat stress of COC reduced blastocyst development (27.2 v. 16.1% for control v. heat stress, respectively; SEM = 1.6; P < 0.002). Approximately 66 and 65% of 24 000 possible genes were called present (i.e. expressed) in RNA from cumulus of competent (control) v. challenged (heat-stressed) oocytes, respectively. In cumulus from developmentally challenged COC, increased abundance of 42 genes (36 currently annotated) was noted. Use of DAVID demonstrated enrichment of genes important for electron transport and energy generation (NOS2A, MAOB, CYP11A1, HSD11B1L, LTB4DH). Further examination of gene ontology identified genes associated with mitochondrial function (SLC25A10, MAOB, CYP11A1), cell signaling (similar to JAK-3, FSHR, CYP11A1, WNT2B), cytoskeleton (ACTA1), antioxidant activity (GSTA1), and extracellular region (FMOD). In contrast, cumulus from developmentally competent COC had increased expression of 22 genes (20 currently annotated), of which 15% were related to protein binding (CAV1, MMP9, TGFB2) according to DAVID. Further analysis using gene ontology revealed genes associated with extracellular matrix formation (MMP9, MMP19, PCOLCE2) and neural tissue (METRNL). In summary, alterations in cumulus gene expression were associated with differences in developmental competence of oocytes. Additional research is necessary to examine the extent to which identified genes account for functional differences in oocyte competence. This research was supported in part by National Research Initiative Competitive Grant no. 2004-35203-14772 from the USDA Cooperative State Research, Education, and Extension Service.

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Efron, Bradley, and Robert Tibshirani. "Empirical bayes methods and false discovery rates for microarrays." Genetic Epidemiology 23, no. 1 (June 2002): 70–86. http://dx.doi.org/10.1002/gepi.1124.

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Won, Joong-Ho, Johan Lim, Donghyeon Yu, Byung Soo Kim, and Kyunga Kim. "Monotone false discovery rate." Statistics & Probability Letters 87 (April 2014): 86–93. http://dx.doi.org/10.1016/j.spl.2013.12.011.

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Tansey, Wesley, Oluwasanmi Koyejo, Russell A. Poldrack, and James G. Scott. "False Discovery Rate Smoothing." Journal of the American Statistical Association 113, no. 523 (June 5, 2018): 1156–71. http://dx.doi.org/10.1080/01621459.2017.1319838.

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Benjamini, Yoav. "Discovering the false discovery rate." Journal of the Royal Statistical Society: Series B (Statistical Methodology) 72, no. 4 (August 5, 2010): 405–16. http://dx.doi.org/10.1111/j.1467-9868.2010.00746.x.

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32

Pounds, S., and C. Cheng. "Improving false discovery rate estimation." Bioinformatics 20, no. 11 (February 26, 2004): 1737–45. http://dx.doi.org/10.1093/bioinformatics/bth160.

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33

Javanmard, Adel, and Andrea Montanari. "Online rules for control of false discovery rate and false discovery exceedance." Annals of Statistics 46, no. 2 (April 2018): 526–54. http://dx.doi.org/10.1214/17-aos1559.

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34

Olsen, Niels Lundtorp, Alessia Pini, and Simone Vantini. "False discovery rate for functional data." TEST 30, no. 3 (January 25, 2021): 784–809. http://dx.doi.org/10.1007/s11749-020-00751-x.

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35

Hu, James X., Hongyu Zhao, and Harrison H. Zhou. "False Discovery Rate Control With Groups." Journal of the American Statistical Association 105, no. 491 (September 1, 2010): 1215–27. http://dx.doi.org/10.1198/jasa.2010.tm09329.

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Yekutieli, Daniel. "Hierarchical False Discovery Rate–Controlling Methodology." Journal of the American Statistical Association 103, no. 481 (March 1, 2008): 309–16. http://dx.doi.org/10.1198/016214507000001373.

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37

Holte, Sarah E., Eva K. Lee, and Yajun Mei. "Symmetric directional false discovery rate control." Statistical Methodology 33 (December 2016): 71–82. http://dx.doi.org/10.1016/j.stamet.2016.08.002.

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BAR-HEN, AVNER, KYUNG IN KIM, and MARK A. VAN DE WIEL. "SOME COMMENTS ON FALSE DISCOVERY RATE." Journal of Bioinformatics and Computational Biology 05, no. 04 (August 2007): 987–90. http://dx.doi.org/10.1142/s0219720007003016.

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39

Siegmund, D. O., N. R. Zhang, and B. Yakir. "False discovery rate for scanning statistics." Biometrika 98, no. 4 (November 24, 2011): 979–85. http://dx.doi.org/10.1093/biomet/asr057.

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40

Finner, Helmut, Thorsten Dickhaus, and Markus Roters. "Dependency and false discovery rate: Asymptotics." Annals of Statistics 35, no. 4 (August 2007): 1432–55. http://dx.doi.org/10.1214/009053607000000046.

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41

Sarkar, Sanat K., and Wenge Guo. "On a generalized false discovery rate." Annals of Statistics 37, no. 3 (June 2009): 1545–65. http://dx.doi.org/10.1214/08-aos617.

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42

Patel, Parth, Srinivas Nallandhighal, David Scoville, Lynn Tran, Brittney Cotta, Aaron M. Udager, Arvind Rao, et al. "The role of spatial transcriptomic profiling to determine androgen receptor signaling and immune infiltration in prostate cancer." Journal of Clinical Oncology 40, no. 6_suppl (February 20, 2022): 272. http://dx.doi.org/10.1200/jco.2022.40.6_suppl.272.

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272 Background: Prostate cancer (PCa) in the transition zone (TZ) accounts for approximately 30% of disease and tends to present with higher PSAs with a lower risk of seminal vesicle invasion, extra-capsular extension, and risk of biochemical recurrence compared with peripheral zone (PZ) tumors. The underlying biological mechanism for these differences is poorly understood. Here, we performed spatial transcriptomic profiling to elucidate the molecular differences between TZ and PZ PCa. Methods: We identified three patients who underwent radical prostatectomy for PCa (one each with PZ only, TZ only, and both PZ and TZ tumors) and used the NanoString’s Digital Spatial Profiling (DSP) platform to quantify whole transcriptomic gene expression data (17,128 genes) in multiple regions of interest (ROI) per patient (42 cancer and 8 normal samples). Four morphology markers to facilitate ROI selection in both cancer and normal areas (SYTO13 for nucleus, PanCK for epithelium, SMA for stroma and CD45 for immune cells) were selected. Raw counts for 17,128 genes were imported to R v.4.1.0 for downstream data analyses. Counts were Q3 normalized and scaled (Z-score) to enable plotting of all genes on the same axes. Differential gene expression analysis using a linear model fit by empirical Bayes moderation, gene set enrichment analysis (GSEA) by cancer hallmarks, XCell gene sets for pathway enrichment and immune cell deconvolution using CIBERSORT was performed. Results: There were grade group (GG) 4 (n=10) and 5 (n=10) tumors in PZ and GG 3 (n=10), 4 (n=11) and 5 (n=1) cancers in TZ regions. We observed distinct gene expression profiles between PZ (n = 20) and TZ (n=22) tumors. Interestingly, androgen receptor (AR) signaling was significantly higher in TZ PCa ROIs compared to PZ ROIs in both GSEA (false discovery rate < 5%) and the androgen subcomponent of the genomic prostate score (p<0.001), regardless of grade, epithelial, stromal or immune component of the region. To standardize the comparison, CIBERSORT’s absolute immune signature scores were only computed for GG4 tumors and found to be significantly higher in PZ GG4 tumors compared to TZ GG4 tumors. Notably, CD4+ memory T cells were significantly higher in PZ GG4 regions compared to TZ GG4 tumor regions (p<0.05). Conclusions: Here, we demonstrate distinct gene expression profiles of PZ and TZ PCa. Specifically, we observed higher AR signaling in TZ cancers and higher levels of immune infiltration on PZ cancers. This is in concordance with prior knowledge that TZ tumors may be associated with higher serum PSA and PZ tumors may be associated with inflammation. Further studies are needed to discern the biological and clinical significance of the different molecular features of PZ and TZ PCa.

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Babu, Varshini, Jane A. Little, Claudia R. Morris, Roberto Machado, J. Simon R. Gibbs, Gregory J. Kato, Victor R. Gordeuk, Mark T. Gladwin, Yingze Zhang, and Seyed Mehdi Nouraie. "Targeted Proteomics of Pulmonary Hypertension in Sickle Cell Disease." Blood 138, Supplement 1 (November 5, 2021): 981. http://dx.doi.org/10.1182/blood-2021-145645.

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Abstract BACKGROUND AND AIM: Hemolysis, inflammation and coagulopathy associated with sickle cell disease (SCD) can lead to pulmonary hypertension. More than 15% of adult patients with SCD are affected by pulmonary hypertension as measured by a tricuspid regurgitation velocity (TRV) ≥ 2.9 m/sec (Nouraie, M. et al., Validation of a composite vascular high-risk profile for adult patients with sickle cell disease. American Journal of Hematology, 94:E312-E314. 2019). Moreover, sickle cell disease patients with pulmonary hypertension have a much higher mortality risk than those without pulmonary hypertension. There is little knowledge on the proteomic profile of pulmonary hypertension in SCD. Our aim was to conduct a proof-of-concept study to explore targeted serum protein biomarkers that are differentially expressed in SCD patients with elevated tricuspid regurgitation velocities and explore their corresponding pathways. MATERIAL AND METHODS: Data from the Walk-PHaSST study was used for this study. The Walk-PHaSST was a multicenter study to assess the effect of sildenafil for SCD patients with pulmonary hypertension (Gladwin, M. T. et al., Risk factors for death in 632 patients with sickle cell disease in the United States and United Kingdom. PLoS One, 9:e99489. 2014). In the screening phase of the study, 720 patients with SCD were recruited. Study participants underwent clinical and lab examinations, as well as an echocardiography. We selected two groups of patients: one group of patients with a TRV≤2.6 m/sec and another with TRV≥2.9 m/sec (N =35 in each). The serum concentration of 92 protein biomarkers was measured using an OLINK cardiovascular panel. This panel assess a range of biological process including inflammatory response, angiogenesis, cell adhesion, coagulation, and response to hypoxia. T-tests were performed between the aforementioned groups for each of the serum biomarkers. The Hochberg method was used to calculate the false discovery rate. A volcano plot was created using the Bonferroni correction. Finally, we tested the correlation of the differentially expressed biomarkers with clinical variables measured in blood samples of the participants. These variables include white blood cell (WBC), neutrophil count, thrombospondin-1 (TSP as measure of coagulation), PIGF and VEGF (growth factors involved in angiogenesis), and NT-proBNP (elevated in pulmonary hypertension). RESULTS: Using a false discovery rate of 0.01, we discovered 14 significantly expressed proteins. Six of them passed a Bonferroni corrected overall critical p value < 0.00054 (Figure 1). These included T-cell surface glycoprotein (CD4), lymphotactin (XCL1), SLAM family member 7 (SLAMF7), galectin-9 (GAL9), TNF-related apoptosis-inducing ligand receptor 2 (TRAILR2), and tumor necrosis factor receptor superfamily member 11A (TNFRSF11A). We observed up to a 1.2-fold increase in these 6 protein biomarker levels in the high TRV groups. The correlogram (Figure 2) indicated that there is a slightly positive correlation between WBC and CD4, GAL9, TRAILR2, and TNFRSF11A (r > 0.20). For neutrophil count, we observed significantly negative correlation with selected markers including TNFRSF11A levels (r = -0.51), GAL9 (r = -0.35), XCL1 (r = -0.27), SLAMF7 (r = - 0.21), and CD4 (r < - 0.24). A significantly negative correlation between TSP and GAL9 levels (r = -0.31) was also observed. Finally, we observed a strong, positive correlation between all proteins and serum NT-proBNP levels (r > 0.44). CONCLUSION: Circulatory protein markers of immune response and coagulation are highly expressed in SCD patients with elevated TRV. This provides evidence that these protein biomarkers have potential to be utilized as prognostic markers for pulmonary hypertension in patients with SCD. Figure 1 Figure 1. Disclosures Little: Biochip Labs: Patents & Royalties; Hemex Health, Inc.: Patents & Royalties. Gibbs: Acceleron Pharma: Consultancy, Other: lecture fees; Actelion: Consultancy, Other: lecture fees; Aerovate: Consultancy, Other: lecture fees; Bayer: Consultancy, Other: lecture fees; Compexia: Consultancy, Other: lecture fees; Janssen: Consultancy, Other: lecture fees; MSD: Consultancy, Other: lecture fees ; Pfizer: Consultancy, Other: lecture fees; United Therapeutics: Consultancy, Other: lecture fees. Gordeuk: Modus Therapeutics: Consultancy; Novartis: Research Funding; Incyte: Research Funding; Emmaus: Consultancy, Research Funding; Global Blood Therapeutics: Consultancy, Research Funding; CSL Behring: Consultancy. Nouraie: Phoenicia BioScience Inc.: Consultancy.

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Melnykov, Igor. "A Positive False Discovery Rate Convergence Result." Communications in Statistics - Theory and Methods 42, no. 23 (December 2, 2013): 4239–46. http://dx.doi.org/10.1080/03610926.2011.648787.

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Genovese, Christopher R., Nicole A. Lazar, and Thomas E. Nichols. "Threshold determination using the false discovery rate." NeuroImage 13, no. 6 (June 2001): 124. http://dx.doi.org/10.1016/s1053-8119(01)91467-3.

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Cerqueti, Roy, and Claudio Lupi. "Copulas, uncertainty, and false discovery rate control." International Journal of Approximate Reasoning 100 (September 2018): 105–14. http://dx.doi.org/10.1016/j.ijar.2018.06.002.

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Bodnar, Taras, and Thorsten Dickhaus. "False discovery rate control under Archimedean copula." Electronic Journal of Statistics 8, no. 2 (2014): 2207–41. http://dx.doi.org/10.1214/14-ejs950.

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Barber, Rina Foygel, and Emmanuel J. Candès. "Controlling the false discovery rate via knockoffs." Annals of Statistics 43, no. 5 (October 2015): 2055–85. http://dx.doi.org/10.1214/15-aos1337.

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Roquain, Etienne, and Mark A. van de Wiel. "Optimal weighting for false discovery rate control." Electronic Journal of Statistics 3 (2009): 678–711. http://dx.doi.org/10.1214/09-ejs430.

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Pounds, S., and C. Cheng. "Robust estimation of the false discovery rate." Bioinformatics 22, no. 16 (June 15, 2006): 1979–87. http://dx.doi.org/10.1093/bioinformatics/btl328.

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