Results 21 to 30 of about 8,023,314 (274)

Benchmarking joint multi-omics dimensionality reduction approaches for the study of cancer

Nature Communications, 2021
High-dimensional multi-omics data are now standard in biology. They can greatly enhance our understanding of biological systems when effectively integrated.
Laura Cantini, Pooya Zakeri, Céline Hernandez, A. Naldi, D. Thieffry, É. Remy, A. Baudot   +6 more
semanticscholar   +1 more source

Dimensionality reduction methods [PDF]

Advances in Methodology and Statistics, 2005
In case one or more sets of variables are available, the use of dimensional reduction methods could be necessary. In this contest, after a review on the link between the Shrinkage Regression Methods and Dimensional Reduction Methods, authors provide a different multivariate extension of the Garthwaite's PLS approach (1994) where a simple linear ...
D'AMBRA L, AMENTA P, GALLO, Michele
openaire   +4 more sources

Evaluating dimensionality reduction for genomic prediction

Frontiers in Genetics, 2022
The development of genomic selection (GS) methods has allowed plant breeding programs to select favorable lines using genomic data before performing field trials.
Vamsi Manthena, Diego Jarquín, Rajeev K. Varshney, Rajeev K. Varshney, Manish Roorkiwal, Manish Roorkiwal, Girish Prasad Dixit, Chellapilla Bharadwaj, Reka Howard   +8 more
doaj   +1 more source

Dimensionality reduction by UMAP reinforces sample heterogeneity analysis in bulk transcriptomic data

bioRxiv, 2021
Transcriptome profiling and differential gene expression constitute a ubiquitous tool in biomedical research and clinical application. Linear dimensionality reduction methods especially principal component analysis (PCA) are widely used in detecting ...
Yang Yang, Hongjian Sun, Yu Zhang, Tiefu Zhang, Jialei Gong, Yunbo Wei, Y. Duan, M. Shu, Yuchen Yang, Di Wu, Di Yu   +10 more
semanticscholar   +1 more source

Haisu: Hierarchically supervised nonlinear dimensionality reduction.

PLoS Computational Biology, 2022
We propose a novel strategy for incorporating hierarchical supervised label information into nonlinear dimensionality reduction techniques. Specifically, we extend t-SNE, UMAP, and PHATE to include known or predicted class labels and demonstrate the ...
Kevin Christopher VanHorn, Murat Can Çobanoğlu   +1 more
doaj   +1 more source

Analyzing Quality Measurements for Dimensionality Reduction

Machine Learning and Knowledge Extraction, 2023
Dimensionality reduction methods can be used to project high-dimensional data into low-dimensional space. If the output space is restricted to two dimensions, the result is a scatter plot whose goal is to present insightful visualizations of distance ...
Michael C. Thrun, Julian Märte, Quirin Stier   +2 more
doaj   +1 more source

A Comparison for Dimensionality Reduction Methods of Single-Cell RNA-seq Data

Frontiers in Genetics, 2021
Single-cell RNA sequencing (scRNA-seq) is a high-throughput sequencing technology performed at the level of an individual cell, which can have a potential to understand cellular heterogeneity.
Ruizhi Xiang, Wencan Wang, Lei Yang, Shiyuan Wang, Chaohan Xu, Xiaowen Chen   +5 more
semanticscholar   +1 more source

2D Dimensionality Reduction Methods without Loss [PDF]

Journal of Artificial Intelligence and Data Mining, 2019
In this paper, several two-dimensional extensions of principal component analysis (PCA) and linear discriminant analysis (LDA) techniques has been applied in a lossless dimensionality reduction framework, for face recognition application.
S. Ahmadkhani, P. Adibi, A. ahmadkhani
doaj   +1 more source

A Comprehensive Review of Dimensionality Reduction Techniques for Feature Selection and Feature Extraction

Journal of Applied Science and Technology Trends, 2020
Due to sharp increases in data dimensions, working on every data mining or machine learning (ML) task requires more efficient techniques to get the desired results.
R. Zebari, A. Abdulazeez, Diyar Qader Zeebaree, D. A. Zebari, J. Saeed   +4 more
semanticscholar   +1 more source

UMAP as a Dimensionality Reduction Tool for Molecular Dynamics Simulations of Biomacromolecules: A Comparison Study.

Journal of Physical Chemistry B, 2021
Proteins are the molecular machines of life. The multitude of possible conformations that proteins can adopt determines their free-energy landscapes.
Francesco Trozzi, Xinlei Wang, Peng Tao
semanticscholar   +1 more source