Unlabelled: PCCA (phylogenetic canonical correlation analysis) is a new program for canonical correlation analysis of multivariate, continuously valued data from biological species. Canonical correlation analysis is a technique in which derived variables are obtained from two sets of original variables whereby the correlations between corresponding derived variables are maximized. It is a very useful multivariate statistical method for the calculation and analysis of correlations between character sets. The program controls for species non-independence due to phylogenetic history and computes canonical coefficients, correlations and scores; and conducts hypothesis tests on the canonical correlations. It can also compute a multivariate version of Pagel's lambda, which can then be used in the phylogenetic transformation.
Availability: PCCA is distributed as DOS/Windows, Mac OS X and Linux/Unix executables with a detailed program manual and is freely available on the World Wide Web at: http://anolis.oeb.harvard.edu/~liam/programs/.
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http://dx.doi.org/10.1093/bioinformatics/btn065 | DOI Listing |
Front Bioeng Biotechnol
January 2025
Biomedical Engineering Department, College of Engineering, Imam Abdulrahman Bin Faisal University, Dammam, Saudi Arabia.
Introduction: Color vision deficiency (CVD), a common visual impairment, affects individuals' ability to differentiate between various colors due to malfunctioning or absent color photoreceptors in the retina. Currently available diagnostic tests require a behavioral response, rendering them unsuitable for individuals with limited physical and communication abilities, such as those with locked-in syndrome. This study introduces a novel, non-invasive method that employs brain signals, specifically Steady-State Visually Evoked Potentials (SSVEPs), along with Ishihara plates to diagnose CVD.
View Article and Find Full Text PDFMethods
January 2025
School of Computer Science, Qufu Normal University, Rizhao 276826, China.
Brain imaging genetics aims to explore the association between genetic factors such as single nucleotide polymorphisms (SNPs) and brain imaging quantitative traits (QTs). However, most existing methods do not consider the nonlinear correlations between genotypic and phenotypic data, as well as potential higher-order relationships among subjects when identifying bi-multivariate associations. In this paper, a novel method called deep hyper-Laplacian regularized self-representation learning based structured association analysis (DHRSAA) is proposed which can learn genotype-phenotype associations and obtain relevant biomarkers.
View Article and Find Full Text PDFJ Anim Breed Genet
January 2025
Departamento de Ciencias Agrícolas y Pecuarias, Universidad Francisco de Paula Santander, Cúcuta, Colombia.
We addressed genomic prediction accounting for partial correlation of marker effects, which entails the estimation of the partial correlation network/graph (PCN) and the precision matrix of an unobservable m-dimensional random variable. To this end, we developed a set of statistical models and methods by extending the canonical model selection problem in Gaussian concentration, and directed acyclic graph models. Our frequentist formulations combined existing methods with the EM algorithm and were termed Glasso-EM, Concord-EM and CSCS-EM, whereas our Bayesian formulations corresponded to hierarchical models termed Bayes G-Sel and Bayes DAG-Sel.
View Article and Find Full Text PDFInt J Behav Nutr Phys Act
January 2025
Department of Rehabilitation Medicine, West China Hospital of Sichuan University, Chengdu, China.
Background: This study aims to investigate the associations between signal-level physical activity (PA) features derived from wrist accelerometry data and cognitive status in older adults, and to evaluate their potential predictive value when combined with demographics.
Methods: We analyzed PA data from 3,363 older adults (NHATS: n = 747; NHANES: n = 2,616), with each participant contributing a complete 3-day continuous activity sequence. We extracted the most relevant PA features associated with cognitive function using feature engineering and recursive feature elimination.
Respir Res
January 2025
Department of Thoracic Surgery, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, China.
Background: Pulmonary arterial hypertension (PAH) is a progressive disorder that can lead to right ventricular failure and severe consequences. Despite extensive efforts, limited progress has been made in preventing the progression of PAH. Mitochondrial dysfunction is implicated in the development of PAH, but the key mitochondrial functional alterations in the pathogenesis have yet to be elucidated.
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