A Density-Based Clustering for Gene Expression Data Using Gene Ontology

2018 ◽  
pp. 757-765 ◽  
Author(s):  
Koyel Mandal ◽  
Rosy Sarmah
Keyword(s):  
Gene Expression ◽  
Gene Ontology ◽  
Gene Expression Data ◽  
Expression Data ◽  
Density Based Clustering

scholarly journals Gene Ontology Analysis of 3D Microarray Gene Expression Data using Hybrid PSO Optimization

2019 ◽  
Vol 8 (11) ◽  
pp. 3890-3896
Keyword(s):  
Gene Expression ◽  
Gene Ontology ◽  
Gene Expression Data ◽  
Biological Significance ◽  
High Volume ◽  
Microarray Gene Expression Data ◽  
Expression Data ◽  
Data Mining Technique ◽  
Microarray Gene Expression ◽  
3D Gene

At present, triclustering is the well known data mining technique for analysis of 3D gene expression data (GST). Triclustering is a simultaneously clustering of subset of Gene (G), subset of Sample (S), and over a subset of Time point (T). Triclustering approach identifies a coherent pattern in the 3D gene expression data using Mean Correlation Value (MCV). In this chapter, Hybrid PSO based algorithm is developed for triclustering of 3D gene expression data. This algorithm can effectively find the coherent pattern with high volume of a tricluster. The experimental study is conducted on yeast cycle dataset to study the biological significance of the coherent tricluster using gene ontology tool

scholarly journals ExAtlas: An interactive online tool for meta-analysis of gene expression data

2015 ◽  
Vol 13 (06) ◽  
pp. 1550019 ◽  
Author(s):  
Alexei A. Sharov ◽  
David Schlessinger ◽  
Minoru S. H. Ko
Keyword(s):  
Gene Expression ◽  
Gene Ontology ◽  
Gene Expression Data ◽  
Fixed Effects ◽  
Expression Profiles ◽  
Meta Analysis ◽  
Data Sets ◽  
Expression Data ◽  
Gene Set ◽  
Public Data

We have developed ExAtlas, an on-line software tool for meta-analysis and visualization of gene expression data. In contrast to existing software tools, ExAtlas compares multi-component data sets and generates results for all combinations (e.g. all gene expression profiles versus all Gene Ontology annotations). ExAtlas handles both users’ own data and data extracted semi-automatically from the public repository (GEO/NCBI database). ExAtlas provides a variety of tools for meta-analyses: (1) standard meta-analysis (fixed effects, random effects, z-score, and Fisher’s methods); (2) analyses of global correlations between gene expression data sets; (3) gene set enrichment; (4) gene set overlap; (5) gene association by expression profile; (6) gene specificity; and (7) statistical analysis (ANOVA, pairwise comparison, and PCA). ExAtlas produces graphical outputs, including heatmaps, scatter-plots, bar-charts, and three-dimensional images. Some of the most widely used public data sets (e.g. GNF/BioGPS, Gene Ontology, KEGG, GAD phenotypes, BrainScan, ENCODE ChIP-seq, and protein–protein interaction) are pre-loaded and can be used for functional annotations.

OverDBC: A new density-based clustering method with the ability of detecting overlapped clusters from gene expression data

2015 ◽  
Vol 19 (6) ◽  
pp. 1311-1321 ◽  
Author(s):  
Mansooreh Mirzaie ◽  
Ahmad Barani ◽  
Naser Nematbakkhsh ◽  
Majid Beigi
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Expression Data ◽  
Clustering Method ◽  
Density Based Clustering

Statistical Absolute Evaluation of Gene Ontology Terms with Gene Expression Data

2007 ◽  
pp. 146-157 ◽  
Author(s):  
Pramod K. Gupta ◽  
Ryo Yoshida ◽  
Seiya Imoto ◽  
Rui Yamaguchi ◽  
Satoru Miyano
Keyword(s):  
Gene Expression ◽  
Gene Ontology ◽  
Gene Expression Data ◽  
Expression Data
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