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Articles de revues sur le sujet « Bayes False discovery rate »

Auteur : Grafiati

Publié le 9 mars 2023

Mis à jour le 31 juillet 2025

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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 (2008): 682–93. http://dx.doi.org/10.1016/j.jspi.2007.01.006.

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Hollister, Megan C., and Jeffrey D. Blume. "4497 Accessible False Discovery Rate Computation." Journal of Clinical and Translational Science 4, s1 (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. Optio

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

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SHRINER, DANIEL. "Mapping multiple quantitative trait loci under Bayes error control." Genetics Research 91, no. 3 (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, fa

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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 hie

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Quatto, Piero, Nicolò Margaritella, Isa Costantini, et al. "Brain networks construction using Bayes FDR and average power function." Statistical Methods in Medical Research 29, no. 3 (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 pres

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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 (2017): e1005700. http://dx.doi.org/10.1371/journal.pcbi.1005700.

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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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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 (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 a

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Hossin, Md Murad, F. M. Javed Mehedi Shamrat, Md Rifat Bhuiyan, Rabea Akter Hira, Tamim Khan, and Shourav Molla. "Breast cancer detection: an effective comparison of different machine learning algorithms on the Wisconsin dataset." Bulletin of Electrical Engineering and Informatics 12, no. 4 (2023): 2446–56. http://dx.doi.org/10.11591/beei.v12i4.4448.

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According to the American cancer society, breast cancer is one of the leading causes of women's mortality worldwide. Early identification and treatment are the most effective approaches to halt the spread of this cancer. The objective of this article is to give a comparison of eight machine learning algorithms, including logistic regression (LR), random forest (RF), K-nearest neighbors (KNN), decision tree (DT), ada boost (AB), support vector machine (SVM), gradient boosting (GB), and Gaussian Naive Bayes (GNB) for breast cancer detection. The breast cancer Wisconsin (diagnostic) dataset is be

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Hossin, Md Murad, F. M. Javed Mehedi Shamrat, Md Rifat Bhuiyan, Rabea Akter Hira, Tamim Khan, and Shourav Molla. "Breast cancer detection: an effective comparison of different machine learning algorithms on the Wisconsin dataset." Bulletin of Electrical Engineering and Informatics 12, no. 4 (2023): 2446–56. http://dx.doi.org/10.11591/eei.v12i4.4448.

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According to the American cancer society, breast cancer is one of the leading causes of women's mortality worldwide. Early identification and treatment are the most effective approaches to halt the spread of this cancer. The objective of this article is to give a comparison of eight machine learning algorithms, including logistic regression (LR), random forest (RF), K-nearest neighbors (KNN), decision tree (DT), ada boost (AB), support vector machine (SVM), gradient boosting (GB), and Gaussian Naive Bayes (GNB) for breast cancer detection. The breast cancer Wisconsin (diagnostic) dataset is be

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Xu, Jingxiong, Wei Xu, Jiyeon Choi, et al. "Large-scale whole exome sequencing studies identify two genes,CTSL and APOE, associated with lung cancer." PLOS Genetics 19, no. 9 (2023): e1010902. http://dx.doi.org/10.1371/journal.pgen.1010902.

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Common genetic variants associated with lung cancer have been well studied in the past decade. However, only 12.3% heritability has been explained by these variants. In this study, we investigate the contribution of rare variants (RVs) (minor allele frequency <0.01) to lung cancer through two large whole exome sequencing case-control studies. We first performed gene-based association tests using a novel Bayes Factor statistic in the International Lung Cancer Consortium, the discovery study (European, 1042 cases vs. 881 controls). The top genes identified are further assessed in the UK Bioba

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Munish, Saran, Kumar Yadav Rajan, Maurya Pranjal, Devi Sangeeta, and Nath Tripathi Upendra. "A novel methodology for enhancing intrusion detection system." i-manager’s Journal on Software Engineering 17, no. 4 (2023): 9. http://dx.doi.org/10.26634/jse.17.4.20009.

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An Intrusion Detection System (IDS) monitors network traffic for suspicious activity and alerts when such an activity is discovered. In this study, the NSL-KDD cup 99 dataset was used to evaluate anomaly detection from intruders. Intrusion Detection System, Distributed Denial of Service (DDoS), Deep Belief Network (DBN), Random Forest, Naïve Bayes, Security Attack, Machine Learning. Pre-processing and normalization processes were performed on the dataset with inadequate, noisy, or duplicate data. A hybrid K-means clustering algorithm is used to combine clusters, which are classified using Deep

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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 (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 s

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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 (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.

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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 (2008): 716–44. http://dx.doi.org/10.1002/bimj.200710473.

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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 (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 infe

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Azeez, Nureni Ayofe, and Emad Fadhal. "Classification of Virtual Harassment on Social Networks Using Ensemble Learning Techniques." Applied Sciences 13, no. 7 (2023): 4570. http://dx.doi.org/10.3390/app13074570.

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Background: Internet social media platforms have become quite popular, enabling a wide range of online users to stay in touch with their friends and relatives wherever they are at any time. This has led to a significant increase in virtual crime from the inception of these platforms to the present day. Users are harassed online when confidential information about them is stolen, or when another user posts insulting or offensive comments about them. This has posed a significant threat to online social media users, both mentally and psychologically. Methods: This research compares traditional cl

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Seth, Soumita, Saurav Mallik, Atikul Islam, et al. "Identifying Genetic Signatures from Single-Cell RNA Sequencing Data by Matrix Imputation and Reduced Set Gene Clustering." Mathematics 11, no. 20 (2023): 4315. http://dx.doi.org/10.3390/math11204315.

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In this current era, the identification of both known and novel cell types, the representation of cells, predicting cell fates, classifying various tumor types, and studying heterogeneity in various cells are the key areas of interest in the analysis of single-cell RNA sequencing (scRNA-seq) data. Due to the nature of the data, cluster identification in single-cell sequencing data with high dimensions presents several difficulties. In this paper, we introduce a new framework that combines various strategies such as imputed matrix, minimum redundancy maximum relevance (MRMR) feature selection,

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Andrade, Daniel, and Yuzuru Okajima. "Adaptive covariate acquisition for minimizing total cost of classification." Machine Learning 110, no. 5 (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 re

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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 e

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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 (2017): 10788–99. http://dx.doi.org/10.1080/03610926.2016.1248781.

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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 (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

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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 (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

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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 (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 da

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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 (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 int

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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 (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 pow

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Kim, Jenny J., Mariette Labots, Luigi Marchionni, 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 (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 w

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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 (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 b

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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 (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

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Sebastiani, Paola, Jacqueline N. Milton, Nadia Timofeev, et al. "Genome-Wide Association Study of Stroke in Sickle Cell Anemia." Blood 114, no. 22 (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 str

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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 de

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

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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 (2012): 871–91. http://dx.doi.org/10.1111/j.1467-9868.2012.01033.x.

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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 (2018): 1156–71. http://dx.doi.org/10.1080/01621459.2017.1319838.

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Zhao, Huang-Hong, Zhen Ma, and Dong-Sheng Guan. "Causal role of immune cells in obstructive sleep apnea hypopnea syndrome: Mendelian randomization study." World Journal of Clinical Cases 12, no. 7 (2024): 1227–34. http://dx.doi.org/10.12998/wjcc.v12.i7.1227.

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BACKGROUND Despite being one of the most prevalent sleep disorders, obstructive sleep apnea hypoventilation syndrome (OSAHS) has limited information on its immunologic foundation. The immunological underpinnings of certain major psychiatric diseases have been uncovered in recent years thanks to the extensive use of genome-wide association studies (GWAS) and genotyping techniques using high-density genetic markers (e.g. , SNP or CNVs). But this tactic hasn't yet been applied to OSAHS. Using a Mendelian randomization analysis, we analyzed the causal link between immune cells and the illness in o

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

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

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Babu, Varshini, Jane A. Little, Claudia R. Morris, et al. "Targeted Proteomics of Pulmonary Hypertension in Sickle Cell Disease." Blood 138, Supplement 1 (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 hy

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Patel, Parth, Srinivas Nallandhighal, David Scoville, 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 (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

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

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

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Hu, James X., Hongyu Zhao, and Harrison H. Zhou. "False Discovery Rate Control With Groups." Journal of the American Statistical Association 105, no. 491 (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 (2008): 309–16. http://dx.doi.org/10.1198/016214507000001373.

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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 (2007): 987–90. http://dx.doi.org/10.1142/s0219720007003016.

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

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

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

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