scholarly journals Consensus Clustering for Cancer Gene Expression Data - Large-Scale Analysis using Evidence Accumulation Approach

2017 ◽  
Author(s):  
Isidora Šašić ◽  
Sanja Brdar ◽  
Tatjana Lončar-Turukalo ◽  
Helena Aidos ◽  
Ana Fred
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Large Scale ◽  
Consensus Clustering ◽  
Cancer Gene ◽  
Expression Data ◽  
Scale Analysis ◽  
Evidence Accumulation ◽  
Large Scale Analysis

scholarly journals Comparison of Merging and Meta-Analysis as Alternative Approaches for Integrative Gene Expression Analysis

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Jonatan Taminau ◽  
Cosmin Lazar ◽  
Stijn Meganck ◽  
Ann Nowé
Keyword(s):  
Gene Expression ◽  
Large Scale ◽  
Meta Analysis ◽  
Microarray Gene Expression Data ◽  
Data Sets ◽  
Expression Data ◽  
Scale Analysis ◽  
Microarray Gene Expression ◽  
Large Scale Analysis ◽  
Microarray Gene

An increasing amount of microarray gene expression data sets is available through public repositories. Their huge potential in making new findings is yet to be unlocked by making them available for large-scale analysis. In order to do so it is essential that independent studies designed for similar biological problems can be integrated, so that new insights can be obtained. These insights would remain undiscovered when analyzing the individual data sets because it is well known that the small number of biological samples used per experiment is a bottleneck in genomic analysis. By increasing the number of samples the statistical power is increased and more general and reliable conclusions can be drawn. In this work, two different approaches for conducting large-scale analysis of microarray gene expression data—meta-analysis and data merging—are compared in the context of the identification of cancer-related biomarkers, by analyzing six independent lung cancer studies. Within this study, we investigate the hypothesis that analyzing large cohorts of samples resulting in merging independent data sets designed to study the same biological problem results in lower false discovery rates than analyzing the same data sets within a more conservative meta-analysis approach.

scholarly journals The knowledge of chromosomal sex is important for large-scale analysis of gene expression

2021 ◽  
pp. 100-109
Author(s):  
O.K. Lykhenko ◽  
◽  
M.Yu. Obolenskaya ◽  
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Large Scale ◽  
Differentially Expressed ◽  
Expression Data ◽  
X Chromosomes ◽  
Large Scale Analysis ◽  
Expression Of Genes ◽  
The Difference ◽  
In Pregnancy

The aim of the study was to determine the sex of the fetus in gene expression data lacking this information using expression of the Y-linked genes, and to elucidate the difference between sex-chromosomal-linked gene expression between placental samples with XX and XY genotypes during pregnacy. We have detected 27 differentially expressed sex-chromosomes-linked genes. We have shown that, in most cases, the expression of genes from X-chromosomes in pregnancy carrying baby girls is higher than in pregnancy carrying baby boys, but there are exceptions to this pattern, which must be taken into account in large-scale studies of gene expression. The nature of the difference in gene expression during pregnancy carrying baby girls and boys (positive or ne gative difference) persists during pregnancy, but the magnitude of the difference may remain unchanged or decrease from the first to the third trimester. Taking sex dimorphism into account when analyzing large-scale gene expression data between trimesters of pregnancy increases the number of differentially expressed genes, which improves the informative value of the study and is important for elucidating the pathogenesis of pregnancy complications associated with placental dysfunction.

scholarly journals Graph Convolutional Network for Drug Response Prediction Using Gene Expression Data

Mathematics ◽  
2021 ◽  
Vol 9 (7) ◽  
pp. 772
Author(s):  
Seonghun Kim ◽  
Seockhun Bae ◽  
Yinhua Piao ◽  
Kyuri Jo
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Large Scale ◽  
Drug Response ◽  
Response Prediction ◽  
Biological Data ◽  
Expression Data ◽  
Convolutional Network ◽  
Essential Information ◽  
Protein Protein Interaction

Genomic profiles of cancer patients such as gene expression have become a major source to predict responses to drugs in the era of personalized medicine. As large-scale drug screening data with cancer cell lines are available, a number of computational methods have been developed for drug response prediction. However, few methods incorporate both gene expression data and the biological network, which can harbor essential information about the underlying process of the drug response. We proposed an analysis framework called DrugGCN for prediction of Drug response using a Graph Convolutional Network (GCN). DrugGCN first generates a gene graph by combining a Protein-Protein Interaction (PPI) network and gene expression data with feature selection of drug-related genes, and the GCN model detects the local features such as subnetworks of genes that contribute to the drug response by localized filtering. We demonstrated the effectiveness of DrugGCN using biological data showing its high prediction accuracy among the competing methods.

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