2期

最新文章

• DECODING MICROBIOME DUAL MEDIATION: INTRODUCING ZIMMA FOR ENHANCED ZERO-INFLATED DATA ANALYSIS AI翻译
  作者：Qiao, Xi; Liu, Ruitao; Tang, Xueying; Peterson, Christine B.; Jenq, Robert R.; Zhang, Liangliang
  作者单位：Utah System of Higher Education; University of Utah; University System of Ohio; Case Western Reserve University; University of Arizona; University of Texas System; UTMD Anderson Cancer Center; City of Hope
  摘要：Given the complex interactions between the microbiome, the host, and external factors, causal mediation analysis is essential for unraveling how dysbiosis or microbial imbalance mediates the effects of interventions or environmental exposures on health outcomes. However, zero inflation in microbiome count data complicates high-dimensional mediation analysis, as frequently employed zero-inflated models often struggle to distinguish true zero inflation from the underlying count distribution. To ...
• 3D BIVARIATE SPATIAL MODELLING OF ARGO OCEAN TEMPERATURE AND SALINITY AI翻译
  作者：Salvana, Mary Lai O.; Cao, Jian; Jun, Mik Young
  作者单位：University of Connecticut; University of Houston System; University of Houston
  摘要：Variables within the global oceans can reveal the impacts of a warming climate, as the oceans absorb huge amounts of solar energy. Understanding the joint spatial distribution of key ocean variables is, therefore, essential. In this paper we investigate the spatial dependence structure between ocean temperature and salinity using Argo observations and construct a bivariate spatial model covering from the surface through the ocean interior. We develop a flexible class of multivariate nonstation...
• A PRINCIPAL SUBMANIFOLD-BASED APPROACH FOR CLUSTERING AND MULTISCALE RNA CORRECTION AI翻译
  作者：Wu, Menghao; Yao, Zhigang
  作者单位：National University of Singapore
  摘要：RNA structure determination is essential for understanding its biological functions. However, the reconstruction process often faces challenges, such as atomic clashes, which can lead to inaccurate models. To address these challenges, we introduce the principal submanifold (PSM) approach for analyzing RNA data on a torus. This method provides an accurate, low-dimensional feature representation, overcoming the limitations of previous torus-based methods. By combining PSM with DBSCAN, we propose...
• ROBUST HIGH-THROUGHPUT IMAGING ANALYSIS WITH WASSERSTEIN GEODESIC TRANSFORMATIONS AI翻译
  作者：Hunt, Gregory J.; Gagnon-Bartsch, Johann A.
  作者单位：University of Michigan System; University of Michigan
  摘要：High-throughput cell imaging has been increasingly used in drug discovery to simultaneously profile the morphological response of cells to thousands of compounds using high-resolution microscopy and automated image analysis. Such experiments characterize thousands of image features in millions of cells across thousands of experimental conditions. Analytical difficulties arise with this scale of analysis as many features have distributions with extremely long tails, high skewness, remote outlie...
• DOMAIN-AWARE MATRIX COMPLETION FOR PHENOTYPE IMPUTATION USING ELECTRONIC HEALTH RECORD DATA WITH APPLICATIONS IN GENOMIC RESEARCH AI翻译
  作者：Wu, Hanqing; Lee, Cue Hyunkyu; Abiri, Najmeh; Ionita-laza, Iuliana
  作者单位：Lund University; Columbia University; Malmo University
  摘要：Large-scale biobanks and electronic health records (EHR) offer great opportunities for next-generation genetic studies. However, missing phenotype data is a pervasive feature of EHR, leading to low power of such studies. One promising solution is prediction-powered inference, where statistical or machine learning models are employed to impute phenotypes prior to performing genetic analyses. Although many such methods exist, they tend to be generic and do not incorporate domain-aware knowledge ...
• ADAPTIVE BLOCK-BASED CHANGE-POINT DETECTION FOR SPARSE SPATIALLY CLUSTERED DATA WITH APPLICATIONS IN REMOTE SENSING IMAGING AI翻译
  作者：Moore, Alan; Chu, Lynna; Zhu, Zhengyuan
  作者单位：Iowa State University
  摘要：We present a nonparametric change-point detection approach to detect potentially sparse changes in a time series of high-dimensional observations or non-Euclidean data objects. We target a change in distribution that occurs in a small, unknown subset of dimensions, where these dimensions may be correlated. Our work is motivated by a remote sensing application, where changes occur in small, spatially clustered regions over time. An adaptive block-based change-point detection framework is propos...
• TOPOLOGICAL INFERENCE ON BRAIN NETWORKS WITH APPLICATION TO LESION SYMPTOM MAPPING AI翻译
  作者：Wang, Yuan; Yin, Jian; Riccardi, Nicholas; Den Ouden, Dirk-bart; Fridriksson, Julius; Desai, Rutvik H.
  作者单位：University of South Carolina System; University of South Carolina Columbia; City University of Hong Kong; University of South Carolina System; University of South Carolina Columbia; University of South Carolina System; University of South Carolina Columbia
  摘要：Persistent homology (PH) characterizes the shape of brain networks through persistence features. Group comparison of persistence features from brain networks can be challenging, as they are inherently heterogeneous. A recent scale-space representation of persistence diagram (PD) through heat diffusion reparameterizes using a finite number of Fourier coefficients with respect to the Laplace-Beltrami (LB) eigenfunction expansion of the domain, thus providing a powerful vectorized algebraic repre...
• TARGETED MAXIMUM LIKELIHOOD ESTIMATION FOR INTEGRAL PROJECTION MODELS IN POPULATION ECOLOGY AI翻译
  作者：Zhou, Yunzhe; Hooker, Giles
  作者单位：University of California System; University of California Berkeley; University of Pennsylvania
  摘要：In population ecology, integral projection models (IPMs) are widely used to study population growth and the dynamics of population structure (e.g., age and size distributions). These models typically use data on the growth, survival, and reproduction of marked individuals to parameterize models for each demographic rate. The resulting models can be used to predict changes in the population from one time point to the next and to predict long-term properties such as long-term population growth r...
• HIERARCHICAL MODELING OF LONGITUDINAL BIOMARKER DATA WITH CHANGEPOINT AND FLEXIBLE SIGMOIDAL RESPONSE AI翻译
  作者：Norris, Michelle; Bedrick, Edward J.; Gardner, Ian; Johnson, Wesley
  作者单位：California State University System; California State University Sacramento; University of Arizona; University of Prince Edward Island; University of California System; University of California Irvine
  摘要：We develop a Bayesian hierarchical model for bivariate longitudinal diagnostic outcome data involving testing for the infective agent for Johne's disease (JD). We consider the situation where an imperfect binary test (fecal culture, FC) is repeatedly administered to each individual together with a continuous biomarker (serum ELISA measured as optical density (OD)). For infected individuals we assume the existence of a change-point corresponding to time of infection and posit appropriate change...
• THE BRADLEY-TERRY STOCHASTIC BLOCK MODEL AI翻译
  作者：Santi, Lapo; Friel, Nial
  作者单位：University College Dublin; University College Dublin
  摘要：The Bradley-Terry model is widely used for the analysis of pairwise comparison data and, in essence, produces a ranking of the items under comparison. We embed the Bradley-Terry model within a stochastic block model, allowing items to cluster. The resulting Bradley-Terry SBM (BT-SBM) ranks clusters so that items within a cluster share the same tied rank. We develop a fully Bayesian specification in which all quantities-the number of blocks, their strengths, and item assignments-are jointly lea...