15 papers · ranked by Valyu relevance
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…
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…
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…
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…
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…
Lin Song, Peter Langfelder, Steve Horvath
Background Ensemble predictors such as the random forest are known to have superior accuracy but their black-box predictions are difficult to interpret. In contrast, a generalized linear model (GLM) is very interpretable especially when forward feature selection is used to construct the model. However, forward feature…
Pierre de Villemereuil, Holger Schielzeth, Shinichi Nakagawa, Michael Morrissey
'Michael Morrissey'] Methods for inference and interpretation of evolutionary quantitative genetic parameters, and for prediction of the response to selection, are best developed for traits with normal distributions. Many traits of evolutionary interest, including many life history and behavioral traits, have…
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…
Boby Mathew, Jens Léon, Mikko J. Sillanpää, Dragan Perovic
In plant breeding, one of the main purpose of multi-environment trial (MET) is to assess the intensity of genotype-by-environment (G×E) interactions in order to select high-performing lines of each environment. Most models to analyze such MET data consider only the additive genetic effects and the part of the…
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…
Ilyas Bakbergenuly, Elena Kulinskaya
Background Systematic reviews and meta-analyses of binary outcomes are widespread in all areas of application. The odds ratio, in particular, is by far the most popular effect measure. However, the standard meta-analysis of odds ratios using a random-effects model has a number of potential problems. An attractive…
Camilo A. Cárdenas-Hurtado, Irini Moustaki, Yunxiao Chen, Giampiero Marra
We introduce a general framework for latent variable modeling, named Generalized Latent Variable Models for Location, Scale, and Shape parameters (GLVM-LSS). This framework extends the generalized linear latent variable model beyond the exponential family distributional assumption and enables the modeling of…
Cristian G. Bologa, Vernon Shane Pankratz, Mark L. Unruh, Maria Eleni Roumelioti + 5 more
'Maria Eleni Roumelioti' 'Vallabh Shah' 'Saeed Kamran Shaffi' 'Soraya Arzhan' 'John Cook' 'Christos Argyropoulos'] Background Converting electronic health record (EHR) entries to useful clinical inferences requires one to address the poor scalability of existing implementations of Generalized Linear Mixed Models (GLMM)…
Zarina I. Vakhitova, Clair L. Alston-Knox, Yannick Griep
In the context of generalized linear models (GLMs), interactions are automatically induced on the natural scale of the data. The conventional approach to measuring effects in GLMs based on significance testing (e.g. the Wald test or using deviance to assess model fit) is not always appropriate. The objective of this…