Bayesian Joint Analysis of Gene Expression Data and Gene Functional Annotations

2012 ◽  
Vol 4 (2) ◽  
pp. 300-318 ◽  
Author(s):  
Xinlei Wang ◽  
Min Chen ◽  
Arkady B. Khodursky ◽  
Guanghua Xiao
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Joint Analysis ◽  
Expression Data ◽  
Functional Annotations

Integrating biological knowledge based on functional annotations for biclustering of gene expression data

2015 ◽  
Vol 119 (3) ◽  
pp. 163-180 ◽  
Author(s):  
Juan A. Nepomuceno ◽  
Alicia Troncoso ◽  
Isabel A. Nepomuceno-Chamorro ◽  
Jesús S. Aguilar-Ruiz
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Biological Knowledge ◽  
Expression Data ◽  
Functional Annotations ◽  
Knowledge Based

scholarly journals Vertical integration methods for gene expression data analysis

2020 ◽  
Author(s):  
Mengyun Wu ◽  
Huangdi Yi ◽  
Shuangge Ma
Keyword(s):  
Gene Expression ◽  
Data Integration ◽  
Gene Expression Data ◽  
Vertical Integration ◽  
Joint Analysis ◽  
Expression Data ◽  
Gene Expressions ◽  
Integration Methods ◽  
Lack Of Information ◽  
Vertical Data

Abstract Gene expression data have played an essential role in many biomedical studies. When the number of genes is large and sample size is limited, there is a ‘lack of information’ problem, leading to low-quality findings. To tackle this problem, both horizontal and vertical data integrations have been developed, where vertical integration methods collectively analyze data on gene expressions as well as their regulators (such as mutations, DNA methylation and miRNAs). In this article, we conduct a selective review of vertical data integration methods for gene expression data. The reviewed methods cover both marginal and joint analysis and supervised and unsupervised analysis. The main goal is to provide a sketch of the vertical data integration paradigm without digging into too many technical details. We also briefly discuss potential pitfalls, directions for future developments and application notes.

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