26 papers · ranked by Valyu relevance
Himel Mallick, Ali Rahnavard, Lauren J. McIver, Siyuan Ma + 16 more
It is challenging to associate features such as human health outcomes, diet, environmental conditions, or other metadata to microbial community measurements, due in part to their quantitative properties. Microbiome multi-omics are typically noisy, sparse (zero-inflated), high-dimensional, extremely non-normal, and…
Dan Jackson, Richard Riley, Ian R White
The multivariate random effects model is a generalization of the standard univariate model. Multivariate meta-analysis is becoming more commonly used and the techniques and related computer software, although continually under development, are now in place. In order to raise awareness of the multivariate methods, and…
Elyas Heidari, Vahid Balazadeh-Meresht, Ali Sharifi‐Zarchi
Increased application of multivariate data in many scientific areas has considerably raised the complexity of analysis and interpretation. Although quite a few approaches have been suggested to address this issue, there is still a gap between the most efficient proposed methods and available software. muvis is an R…
Rodney A Hayward, David M Kent, Sandeep Vijan, Timothy P Hofer
Background When subgroup analyses of a positive clinical trial are unrevealing, such findings are commonly used to argue that the treatment's benefits apply to the entire study population; however, such analyses are often limited by poor statistical power. Multivariable risk-stratified analysis has been proposed as an…
Martin N. Hebart, Chris I. Baker
Multivariate decoding methods were developed originally as tools to enable accurate predictions in real-world applications. The realization that these methods can also be employed to study brain function has led to their widespread adoption in the neurosciences. However, prior to the rise of multivariate decoding, the…
Glaucia Cristina Rodrigues Nascimento, Marcela Baraúna Magno, Giseon Heo, David Normando
High-dimensional data hinder sample visualization and limit exploration of data1. In these cases, we can make use of multivariate analysis techniques, such as Factor Analysis (FA) and/or Principal Component Analysis (PCA), to reduce a complex data set to one of lower dimensions so as to reveal any hidden features and…
Ellen F. Mosleth, Kristian Hovde Liland
Modern analysis technologies output large amounts of multivariate data. The data may come from an experimental design or from other collected observations. We here present a flexible tool, called General Effect Modelling (GEM), for the analysis of any type of multivariate data influenced by one or more qualitative…
Pär Jonsson, Benny Björkblom, Elin Chorell, Tommy Olsson + 1 more
Multivariate projection methods are unique in being both multivariable by combining many variables into stronger predictive features (latent variables), and multivariate for being able to model systematic variation both related and orthogonal to an observed response. Orthogonal partial least squares (OPLS) is a…
Asokan Mulayath Variyath, Anita Brobbey, Feng Chen
Multivariate multiple regression analysis is often used to assess covariate effects when one or multiple response variables are collected in observational or experimental studies. Many multivariate regression techniques are designed for univariate responses. A common way to deal with multiple response variables is to…
Verena Zuber, Johanna Maria Colijn, Caroline Klaver, Stephen Burgess
Modern high-throughput experiments provide a rich resource to investigate causal determinants of disease risk. Mendelian randomization (MR) is the use of genetic variants as instrumental variables to infer the causal effect of a specific risk factor on an outcome. Multivariable MR is an extension of the standard MR…
Noah Lorincz-Comi, Yihe Yang, Gen Li, Xiaofeng Zhu
Mendelian randomization (MR) is an instrumental variable approach used to infer causal relationships between exposures and outcomes and can apply to summary data from genome-wide association studies (GWAS). Since GWAS summary statistics are subject to estimation errors, most existing MR approaches suffer from…
Steven Geinitz, Reinhard Furrer, Stephan R. Sain
Classical analysis of variance requires that model terms be labeled as fixed or random and typically culminate by comparing variability from each batch (factor) to variability from errors; without a standard methodology to assess the magnitude of a batch's variability, to compare variability between batches, nor to…
D. G. O’Neill, C. Pegram, P. Crocker, D. C. Brodbelt + 2 more
'R. M. A. Packer'] Brachycephalic dog breeds are regularly asserted as being less healthy than non-brachycephalic breeds. Using primary-care veterinary clinical data, this study aimed to identify predispositions and protections in brachycephalic dogs and explore differing inferences between univariable and…
Christopher James Rose, Unni Olsen, Maren Falch Lindberg, Eva Denison + 2 more
'Eva Denison' 'Arild Aamodt' 'Anners Lerdal'] Studies often estimate associations between an outcome and multiple variates. For example, studies of diagnostic test accuracy estimate sensitivity and specificity, and studies of predictive and prognostic factors typically estimate associations for multiple factors.…
Mark de Rooij, Ligaya Breemer, Dion Woestenburg, Frank Busing
We present a multidimensional data analysis framework for the analysis of ordinal response variables. Underlying the ordinal variables, we assume a continuous latent variable, leading to cumulative logit models. The framework includes unsupervised methods, when no predictor variables are available, and supervised…
Rui Portocarrero Sarmento, Vera L. Costa
The use of statistical software in academia and enterprises has been evolving over the last years. More often than not, students, professors, workers and users in general have all had, at some point, exposure to statistical software. Sometimes, difficulties are felt when dealing with such type of software. Very few…
Claire Prince, Laura D. Howe, Gemma C. Sharp, Abigail Fraser + 1 more
'Rebecca C. Richmond'] Background Few studies have investigated associations between adiposity and reproductive factors using causal methods, both of which have a number of consequences on women’s health. Here we assess whether adiposity at different points in the lifecourse affects reproductive factors differently and…
Authors not listed
The analysis of nonadiabatic molecular dynamics (NAMD) data presents significant challenges due to its high dimensionality and complexity. To address these issues, we introduce ULaMDyn, a Python-based, open-source package designed to automate the unsupervised analysis of large datasets generated by NAMD simulations.…
Authors not listed
Decades of extensive research have proved that β-amyloid (Aβ) peptides and their aggregation, inducing oxidative stress in the brain, play a key role in Alzheimer’s disease (AD) development. Moreover, Aβ peptides bind to Cu(II) ions, and the resulting complexes accelerate the aggregation process while promoting the…
Authors not listed
The SCF part of the HF-SCF method is responsible for finding the ground state as the global minimum of the one-determinant approximation of the electronic energy, which is a 4th order multivariable polynomial of the LCAO coefficients and Lagrange multipliers. In this work we replace this SCF part with algebraic…
S. Ghazaleh Dashti, Katherine J. Lee, J. A. Simpson, John B. Carlin + 1 more
S. Ghazaleh Dashti1,2, Katherine J. Lee1,2, Julie A. Simpson3,4, John B. Carlin1,2,3, Margarita Moreno-Betancur1,2 1 Department of Pediatrics, University of Melbourne, Melbourne, Victoria, Australia 2 Clinical Epidemiology and Biostatistics Unit, Murdoch Children's Research Institute, Melbourne, Victoria, Australia 3…
Steven Geinitz, Reinhard Furrer
Assessing variability according to distinct factors in data is a fundamental technique of statistics. The method commonly regarded to as analysis of variance (ANOVA) is, however, typically confined to the case where all levels of a factor are present in the data (i.e. the population of factor levels has been…
Pablo Quijano Velasco, Kedar Hippalgaonkar, Balamurugan Ramalingam
The discovery of optimal conditions of chemical reactions is a labor-intensive, time-consuming task that requires exploring a high-dimensional parametric space. Historically the optimization of chemical reactions has been performed by manual experimentation guided by human intuition and Design of Experiments where one…
Authors not listed
Deciphering the correct mechanism governing certain phenomenon in polyelectrolyte (PE) brush grafted systems, revealed through atomistic simulations, is an extremely challenging problem. In a recent study, our all-atom molecular dynamics (MD) simulations revealed a non-linearly large electroosmotic flow (in the…
Andrew McCluskey
The use of mathematical transformations to reduce non-linear functions to linear problems, which can be tackled with analytical linear regression, is commonplace in the chemistry curriculum. The linearization procedure, however, assumes an incorrect statistical model for real experimental data; leading to biased…
Authors not listed
Background: Pharmaceutical batch scheduling in multi-reactor configurations presents complex optimization challenges under operational uncertainty, yet limited research addresses how parallel processing capacity affects heuristic performance and predictive modeling. Objectives: This study investigated scheduling…