Results 1 to 10 of about 220 (90)

Sparse sliced inverse regression for high dimensional data analysis [PDF]

BMC Bioinformatics, 2022
Background Dimension reduction and variable selection play a critical role in the analysis of contemporary high-dimensional data. The semi-parametric multi-index model often serves as a reasonable model for analysis of such high-dimensional data.
Haileab Hilafu, Sandra E. Safo
doaj   +2 more sources

A sliced inverse regression (SIR) decoding the forelimb movement from neuronal spikes in the rat motor cortex [PDF]

Frontiers in Neuroscience, 2016
Several neural decoding algorithms have successfully converted brain signals into commands to control a computer cursor and prosthetic devices. A majority of decoding methods, such as population vector algorithms (PVA), optimal linear estimators (OLE ...
Shih-Hung Yang, You-Yin Chen, Sheng-Huang Lin, Sheng-Huang Lin, Lun-De Liao, Lun-De Liao, Horng-Shing Lu, Ching-Fu Wang, Po-Chuan Chen, Yu-Chun Lo, Thanh Dat Phan, Hsiang-Ya Chao, Hui-Ching Lin, Hsin-Yi Lai, Wei-Chen Huang   +14 more
doaj   +2 more sources

On linear dimension reduction based on diagonalization of scatter matrices for bioinformatics downstream analyses [PDF]

Heliyon, 2020
Dimension reduction is often a preliminary step in the analysis of data sets with a large number of variables. Most classical, both supervised and unsupervised, dimension reduction methods such as principal component analysis (PCA), independent component
Daniel Fischer, Klaus Nordhausen, Hannu Oja   +2 more
doaj   +2 more sources

Multiple phenotype association tests based on sliced inverse regression [PDF]

BMC Bioinformatics
Background Joint analysis of multiple phenotypes in studies of biological systems such as Genome-Wide Association Studies is critical to revealing the functional interactions between various traits and genetic variants, but growth of data in ...
Wenyuan Sun, Kyongson Jon, Wensheng Zhu, the Alzheimer’s Disease Neuroimaging Initiative   +3 more
doaj   +2 more sources

A Comparative Study of Five Association Tests Based on CpG Set for Epigenome-Wide Association Studies. [PDF]

PLoS ONE, 2016
An epigenome-wide association study (EWAS) is a large-scale study of human disease-associated epigenetic variation, specifically variation in DNA methylation.
Qiuyi Zhang, Yang Zhao, Ruyang Zhang, Yongyue Wei, Honggang Yi, Fang Shao, Feng Chen   +6 more
doaj   +2 more sources

ePoster. [PDF]

Eur J Neurol
European Journal of Neurology, Volume 33, Issue S1, June 2026.
europepmc   +2 more sources

SNP set association analysis for genome-wide association studies. [PDF]

PLoS ONE, 2013
Genome-wide association study (GWAS) is a promising approach for identifying common genetic variants of the diseases on the basis of millions of single nucleotide polymorphisms (SNPs).
Min Cai, Hui Dai, Yongyong Qiu, Yang Zhao, Ruyang Zhang, Minjie Chu, Juncheng Dai, Zhibin Hu, Hongbing Shen, Feng Chen   +9 more
doaj   +1 more source

Dimension Reduction Regression in R

Journal of Statistical Software, 2002
Regression is the study of the dependence of a response variable y on a collection predictors p collected in x. In dimension reduction regression, we seek to find a few linear combinations β1x,...,βdx, such that all the information about the regression ...
Sanford Weisberg
doaj   +1 more source

Sliced Inverse Regression: application to fundamental stellar parameters

Open Astronomy, 2019
We present a method for deriving the stellar fundamental parameters. It is based on a regularized sliced inverse regression (RSIR).We first tested it on noisy synthetic spectra of A, F, G, and K-type stars, and inverted simultaneously their atmospheric ...
Kassounian Sarkis, Gebran Marwan, Paletou Frédéric, Watson Victor   +3 more
doaj   +1 more source

MR Derived Cardiac Metabolism Changes in Patients With Obesity and Diabetes: Knowledge Discovery Via Bayesian Networks and Random Forest Classification

NMR in Biomedicine, Volume 39, Issue 10, October 2026.
Characterization of healthy vs. diabetic (73:67$$ 73:67 $$) random forest classification group and its Bayesian network learned through NOTEARS structure algorithm on multi‐modal data. Shown computed SHapley Additive exPlanation (SHAP) values for various features.
Ina Hanninger, Andrej Krafčík, Eylem Levelt, Jennifer J Rayner, Chistopher T Rodgers, Stefan Neubauer, Vicente Grau, Oliver J Rider, Ladislav Valkovič   +8 more
wiley   +1 more source