Results 31 to 40 of about 2,557,835 (263)
Categorical Data Analysis for High-Dimensional Sparse Gene Expression Data
BioTech, 2023 Categorical data analysis becomes challenging when high-dimensional sparse covariates are involved, which is often the case for omics data. We introduce a statistical procedure based on multinomial logistic regression analysis for such scenarios ...
Niloufar Dousti Mousavi +2 more
doaj +1 more source
Discretization of gene expression data revised [PDF]
Briefings in Bioinformatics, 2015 Gene expression measurements represent the most important source of biological data used to unveil the interaction and functionality of genes. In this regard, several data mining and machine learning algorithms have been proposed that require, in a number of cases, some kind of data discretization to perform the inference.
Cristian Andrés Gallo +4 more
openaire +3 more sources
A Microarray Data Pre-processing Method for Cancer Classification
JOIV: International Journal on Informatics Visualization, 2022 The development of microarray technology has led to significant improvements and research in various fields. With the help of machine learning techniques and statistical methods, it is now possible to organize, analyze, and interpret large amounts of ...
Tay Xin Hui +5 more
doaj +1 more source
Computational and Structural Biotechnology Journal, 2022 Weighted gene co-expression network analysis (WGCNA) is used to detect clusters with highly correlated genes. Measurements of correlation most typically rely on linear relationships.
Tianjiao Zhang, Garry Wong
doaj +1 more source
Bi-dimensional principal gene feature selection from big gene expression data
PLoS ONE, 2022 Gene expression sample data, which usually contains massive expression profiles of genes, is commonly used for disease related gene analysis. The selection of relevant genes from huge amount of genes is always a fundamental process in applications of ...
Xiaoqian Hou, Jingyu Hou, Guangyan Huang
doaj +2 more sources
On Differential Gene Expression Using RNA-Seq Data
Cancer Informatics, 2011 Motivation RNA-Seq is a novel technology that provides read counts of RNA fragments in each gene, including the mapped positions of each read within each gene. Besides many other applications it can be used to detect differentially expressed genes.
Juhee Lee +4 more
doaj +2 more sources
Clustering gene expression data using a diffraction‐inspired framework
BioMedical Engineering OnLine, 2012 Background The recent developments in microarray technology has allowed for the simultaneous measurement of gene expression levels. The large amount of captured data challenges conventional statistical tools for analysing and finding inherent ...
Dinger Steven C +3 more
doaj +1 more source
GENE BICLUSTERING ON LARGE DATASETS USING FUZZY C-MEANS CLUSTERING
ICTACT Journal on Soft Computing, 2022 The current study employs biclustering to alleviate some of the drawbacks associated with gene expression data grouping. Different biclustering algorithms are used in this study to detect unique gene activity in various contexts and reduce the ...
M Ramkumar +4 more
doaj +1 more source
On the Role of Clustering and Visualization Techniques in Gene Microarray Data
Algorithms, 2019 As of today, bioinformatics is one of the most exciting fields of scientific research. There is a wide-ranging list of challenging problems to face, i.e., pairwise and multiple alignments, motif detection/discrimination/classification, phylogenetic tree ...
Angelo Ciaramella, Antonino Staiano
doaj +1 more source
Validating clustering for gene expression data [PDF]
Bioinformatics, 2001 Abstract Motivation: Many clustering algorithms have been proposed for the analysis of gene expression data, but little guidance is available to help choose among them. We provide a systematic framework for assessing the results of clustering algorithms.
Ka Yee Yeung +2 more
openaire +3 more sources