The Annals of Applied Statistics

最新文章

• MODEL-BASED FEATURE SELECTION AND CLUSTERING OF RNA-SEQ DATA FOR UNSUPERVISED SUBTYPE DISCOVERY AI翻译
  作者：Lim, David K.; Rashid, Naim U.; Ibrahim, Joseph G.
  作者单位：University of North Carolina; University of North Carolina Chapel Hill; University of North Carolina School of Medicine
  摘要：Clustering is a form of unsupervised learning that aims to uncover latent groups within data based on similarity across a set of features. A common application of this in biomedical research is in delineating novel cancer subtypes from patient gene expression data, given a set of informative genes. However, it is typically unknown a priori what genes may be informative in discriminating between clusters and what the optimal number of clusters are. Few methods exist for performing unsupervised ...
• POINT PROCESS MODELING OF DRUG OVERDOSES WITH HETEROGENEOUS AND MISSING DATA AI翻译
  作者：Liu, Xueying; Carter, Jeremy; Ray, Brad; Mohler, George
  作者单位：Purdue University System; Purdue University; Purdue University in Indianapolis; Indiana University System; Indiana University Indianapolis
  摘要：Opioid overdose rates have increased in the United States over the past decade and reflect a major public health crisis. Modeling and prediction of drug and opioid hotspots, where a high percentage of events fall in a small percentage of space-time, could help better focus limited social and health services. In this work we present a spatial-temporal point process model for drug overdose clustering. The data input into the model comes from two heterogeneous sources: (1) high volume emergency m...
• BAYICE: A BAYESIAN HIERARCHICAL MODEL FOR SEMIREFERENCE-BASED DECONVOLUTION OF BULK TRANSCRIPTOMIC DATA AI翻译
  作者：Tai, An-Shun; Tseng, George C.; Hsieh, Wen-Ping
  作者单位：National Tsing Hua University; Pennsylvania Commonwealth System of Higher Education (PCSHE); University of Pittsburgh
  摘要：Gene expression deconvolution is a powerful tool for exploring the microenvironment of complex tissues comprised of multiple cell groups using transcriptomic data. Characterizing cell activities for a particular condition has been regarded as a primary mission against diseases. For example, cancer immunology aims to clarify the role of the immune system in the progression and development of cancer through analyzing the immune cell components of tumors. To that end, many deconvolution methods h...
• BRIDGING RANDOMIZED CONTROLLED TRIALS AND SINGLE-ARM TRIALS USING COMMENSURATE PRIORS IN ARM-BASED NETWORK META-ANALYSIS AI翻译
  作者：Wang, Zhenxun; Lin, Lifeng; Murray, Thomas; Hodges, James S.; Chu, Haitao
  作者单位：University of Minnesota System; University of Minnesota Twin Cities; State University System of Florida; Florida State University
  摘要：Network meta-analysis (NMA) is a powerful tool to compare multiple treatments directly and indirectly by combining and contrasting multiple independent clinical trials. Because many NMAs collect only a few eligible randomized controlled trials (RCTs), there is an urgent need to synthesize different sources of information, for example, from both RCTs and single-arm trials. However, single-arm trials and RCTs may have different populations and quality so that assuming they are exchangeable may b...
• PREDICTION OF THE NASH THROUGH PENALIZED MIXTURE OF LOGISTIC REGRESSION MODELS AI翻译
  作者：Morvan, Marie; Devijver, Emilie; Giacofci, Madison; Monbet, Valerie
  作者单位：Universite de Rennes; Centre National de la Recherche Scientifique (CNRS); CNRS - National Institute for Mathematical Sciences (INSMI); Communaute Universite Grenoble Alpes; Universite Grenoble Alpes (UGA); Inria; Centre National de la Recherche Scientifique (CNRS); Institut National Polytechnique de Grenoble
  摘要：In this paper an appropriate and interpretable diagnosis statistical model is proposed to predict Nonalcoholic Steatohepatitis (NASH) from near infrared spectrometry data. In this disease, unknown patients' profiles are expected to lead to a different diagnosis. The model has then to take into account the heterogeneity of the data and the dimension of the spectrometric data. To this end, we propose to fit a mixture model on the joint distribution of the diagnostic binary variable and the covar...
• TWO-STAGE CIRCULAR-CIRCULAR REGRESSION WITH ZERO INFLATION: APPLICATION TO MEDICAL SCIENCES AI翻译
  作者：Jha, Jayant; Bhuyan, Prajamitra
  作者单位：Aix-Marseille Universite; Assistance Publique-Hopitaux de Marseille; Institut National de la Sante et de la Recherche Medicale (Inserm); Imperial College London
  摘要：This paper considers the modeling of zero-inflated circular measurements concerning real case studies from medical sciences. Circular-circular regression models have been discussed in the statistical literature and illustrated with various real-life applications. However, there are no models to deal with zero-inflated response as well as a covariate simultaneously. The Mobius transformation based two-stage circular-circular regression model is proposed, and the Bayesian estimation of the model...
• ZERO-INFLATED QUANTILE RANK-SCORE BASED TEST (ZIQRANK) WITH APPLICATION TO SCRNA-SEQ DIFFERENTIAL GENE EXPRESSION ANALYSIS AI翻译
  作者：Ling, Wodan; Zhang, Wenfei; Cheng, Bin; Wei, Ying
  作者单位：Fred Hutchinson Cancer Center; Sarepta Therapeutics, Inc.; Columbia University
  摘要：Differential gene expression analysis based on scRNA-seq data is challenging due to two unique characteristics of scRNA-seq data. First, multimodality and other heterogeneity of the gene expression among different cell conditions lead to divergences in the tail events or crossings of the expression distributions. Second, scRNA-seq data generally have a considerable fraction of dropout events, causing zero inflation in the expression. To account for the first characteristic, existing parametric...
• EDITORIAL: STATISTICAL SIGNIFICANCE, P-VALUES, AND REPLICABILITY AI翻译
  作者：Kafadar, Karen
• DEEP LEARNING SEMIPARAMETRIC REGRESSION FOR ADJUSTING COMPLEX CONFOUNDING STRUCTURES AI翻译
  作者：Mi, Xinlei; Tighe, Patrick; Zou, Fei; Zou, Baiming
  作者单位：Northwestern University; State University System of Florida; University of Florida; University of North Carolina; University of North Carolina Chapel Hill
  摘要：Deep Treatment Learning (deepTL), a robust yet efficient deep learning-based semiparametric regression approach, is proposed to adjust the complex confounding structures in comparative effectiveness analysis of observational data, for example, electronic health record (EHR) data in which complex confounding structures are often embedded. Specifically, we develop a deep learning neural network with a score-based ensembling scheme for flexible function approximation. An improved semiparametric p...
• SIMULTANEOUS INFERENCE OF PERIODS AND PERIOD-LUMINOSITY RELATIONS FOR MIRA VARIABLE STARS AI翻译
  作者：He, Shiyuan; Lin, Zhenfeng; Yuan, Wenlong; Macri, Lucas M.; Huang, Jianhua Z.
  作者单位：Renmin University of China; Microsoft; Johns Hopkins University; Texas A&M University System; Texas A&M University College Station; Texas A&M University System; Texas A&M University College Station
  摘要：The period-luminosity relation (PLR) of Mira variable stars is an important tool to determine astronomical distances. The common approach of estimating the PLR is a two-step procedure that first estimates the Mira periods and then runs a linear regression of magnitude on log period. When the light curves are sparse and noisy, the accuracy of period estimation decreases and can suffer from aliasing effects. Some methods improve accuracy by incorporating complex model structures at the expense o...