25 papers · ranked by Valyu relevance
Wagner Hugo Bonat, Bent Jørgensen
Summary. We propose a general framework for non-normal multivariate data analysis called multivariate covariance generalized linear models (McGLMs), designed to handle multivariate response variables, along with a wide range of temporal and spatial correlation structures defined in terms of a covariance link function…
Omid Chatrabgoun, Alireza Daneshkhah, Parisa Torkaman, Mark Johnston + 3 more
'Nader Sohrabi Safa' 'Ali Kashif Bashir' 'Zakariya Yahya Algamal'] Many machine learning techniques have been used to construct gene regulatory networks (GRNs) through precision matrix that considers conditional independence among genes, and finally produces sparse version of GRNs. This construction can be improved…
Jiin Choi, Taesung Park
Background Recently, one of the greatest challenges in genome-wide association studies is to detect gene-gene and/or gene-environment interactions for common complex human diseases. Ritchie et al. (2001) proposed multifactor dimensionality reduction (MDR) method for interaction analysis. MDR is a combinatorial approach…
Fábio S. Ferreira, João M.S. Pereira, João V. Duarte, Miguel Castelo-Branco
'Miguel Castelo-Branco'] Background: Although voxel based morphometry studies are still the standard for analyzing brain structure, their dependence on massive univariate inferential methods is a limiting factor. A better understanding of brain pathologies can be achieved by applying inferential multivariate methods…
Fentaw Abegaz, Davar Abedini, Lemeng Dong, Johan A. Westerhuis + 3 more
In microbiome studies, addressing the unique characteristics of sequence data-such as compositionality, zero inflation, overdispersion, high dimensionality, and non-normality-is crucial for accurate analysis. In addition, integrating experimental design elements into microbiome data analysis is important for…
Benjamin Avanzi, G. B. Taylor, Phuong Anh Vu, Bernard Wong
In this paper, we develop a multivariate evolutionary generalised linear model (GLM) framework for claims reserving, which allows for dynamic features of claims activity in conjunction with dependency across business lines to accurately assess claims reserves. We extend the traditional GLM reserving framework on two…
Guilherme Parreira da Silva, Henrique Aparecido Laureano, Ricardo Rasmussen Petterle, Paulo Justiniano Ribeiro + 1 more
'Ricardo Rasmussen Petterle' 'Paulo Justiniano Ribeiro' 'Wagner Hugo Bonat'] Researchers are often interested in understanding the relationship between a set of covariates and a set of response variables. To achieve this goal, the use of regression analysis, either linear or generalized linear models, is largely…
Jenni Niku, Wesley Brooks, Riki Herliansyah, Francis K. C. Hui + 3 more
'Sara Taskinen' 'David I. Warton' 'Jin Li'] Generalized linear latent variable models (GLLVM) are popular tools for modeling multivariate, correlated responses. Such data are often encountered, for instance, in ecological studies, where presence-absences, counts, or biomass of interacting species are collected from a…
Tien-Wen Lee
The General Linear Model (GLM) has been widely used in research, where error term has been treated as noise. However, compelling evidence suggests that in biological systems, the target variables may possess their innate variances. A modified GLM was proposed to explicitly model biological variance and non-biological…
Joram Soch, Carsten Allefeld, John-Dylan Haynes
Techniques of multivariate pattern analysis (MVPA) can be used to decode the discrete experimental condition or a continuous modulator variable from measured brain activity during a particular trial. In functional magnetic resonance imaging (fMRI), trial-wise response amplitudes are sometimes estimated from the…
Fabio Morgante, Peter Carbonetto, Gao Wang, Yuxin Zou + 2 more
Predicting phenotypes from genotypes is a fundamental task in quantitative genetics. With technological advances, it is now possible to measure multiple phenotypes in large samples. Multiple phenotypes can share their genetic component; therefore, modeling these phenotypes jointly may improve prediction accuracy by…
Jeanett S. Pelck, Rodrigo Labouriau
We propose a method for inference in generalised linear mixed models (GLMMs) and several extensions of these models. First, we extend the GLMM by allowing the distribution of the random components to be non-Gaussian, that is, assuming an absolutely continuous distribution with respect to the Lebesgue measure that is…
M. Gomtsyan, Céline Lévy-Leduc, Sarah Ouadah, Laure Sansonnet + 2 more
'Christophe Bailly' 'Loïc Rajjou'] Abstract. We propose a novel and efficient iterative two-stage variable selection approach for multivariate sparse GLARMA models, which can be used for modelling multivariate discrete-valued time series. Our approach consists in iteratively combining two steps: the estimation of the…
Shao-Hsuan Wang, Ray Bai, Hsin‐Hsiung Huang
In recent years, the literature on Bayesian high-dimensional variable selection has rapidly grown. It is increasingly important to understand whether these Bayesian methods can consistently estimate the model parameters. To this end, shrinkage priors are useful for identifying relevant signals in high-dimensional data.…
Dan Wang, Jun Teng, Changheng Zhao, Xinhao Zhang + 5 more
Current methods of multivariate analysis require complete multivariate phenotypes from each individual and have a computational time complexity of O(n^2^) per SNP, where n is the sample size. We develop an efficient genomic multivariate analysis tool (GMAT) for genome-wide association studies of multiple correlated…
D. Vuckovic, P. Gasparini, N. Soranzo, V. Iotchkova
As new methods for multivariate analysis of Genome Wide Association Studies (GWAS) become available, it is important to be able to combine results from different cohorts in a meta-analysis. The R package MultiMeta provides an implementation of the inverse-variance based method for meta-analysis, generalized to an…
Eric Houngla Adjakossa, Ibrahim Sadissou, Mahouton Norbert Hounkonnou, Gregory Nuel + 1 more
'Gregory Nuel' 'Lourens J Waldorp'] In the context of multivariate multilevel data analysis, this paper focuses on the multivariate linear mixed-effects model, including all the correlations between the random effects when the dimensional residual terms are assumed uncorrelated. Using the EM algorithm, we suggest more…
Sanjeena Subedi, Utkarsh J. Dang
Modern biological data are often multivariate discrete counts, and there has been a dearth of statistical distributions to directly model such counts in an efficient manner. While mixed Poisson distributions, e.g., negative binomial distribution, are often the distribution of choice for univariate data, multivariate…
Anil Aktas Samur, Nesil Coskunfirat, Osman Saka, Søren Bentzen
Longitudinal data with binary repeated responses are now widespread among clinical studies and standard statistical analysis methods have become inadequate in the answering of clinical hypotheses. Instead of such conventional approaches, statisticians have started proposing better techniques, such as the Generalized…
Yuanqing Lu, Timur Fazletdinov, Zhiwen Pan, Katrin Wondraczek + 1 more
The synthesis of nanoscale particles and particle aggregates from liquid or gaseous precursors is affected by a variety of trade-off relations, for example, in terms of product composition, yield, or energy efficiency. Machine-supported process evaluation and learning (ML) of these relations enables optimization…
Authors not listed
We developed OpenStats, a user-friendly web application that brings the power of the R language to researchers through a high-level interface and broad support for statistical methods such as t-tests and ANOVA. OpenStats was integrated into our electronic lab notebook Chemotion ELN via its third-party API, enabling…
Ludwig A. Hothorn
In bio-medical studies, the p-values of the F-tests in ANOVA are usually interpreted independently as measures of the significance of the associated factors. This ’hidden multiplicity’ effect increases the false positive rate. Therefore, Cramer et al. (2016) proposed the Bonferroni adjustment of the p-values to control…
Authors not listed
Inverse problems, where we seek the values of inputs to a model that lead to a desired set of outputs, are a challenges subset of problems in science and engineering. In this work we demonstrate the use of two generative AI methods to solve inverse problems. We compare this approach to two more conventional approaches…
Authors not listed
Ensuring the trustworthiness of machine learning (ML) models in high-stake applications is crucial. One such application is predicting anti-cancer drug sensitivity, where ML models are built with the final goal of integrating them into treatment recommendation systems for personalized medicine. Here, we propose a…
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This paper presents the Multi Cell-line Kinetic Model (MCKM), a novel generalised kinetic mechanistic model specifically tailored for Ambr15™ fed-batch cultivations of multiple Chinese Hamster Ovary (CHO) cell lines producing different recombinant monoclonal antibodies (mAbs). Unlike traditional models that requires…