Limits...
Disease Biomarker Query from RNA-Seq Data.

Han H, Jiang X - Cancer Inform (2014)

Bottom Line: Although there were many biomarker discovery algorithms available in traditional omics communities, they cannot be applied to RNA-Seq count data to seek biomarkers directly for its special characteristics.In this work, we have presented a biomarker discovery algorithm, SEQ-Marker for RNA-Seq data, which is built on a novel data-driven feature selection algorithm, nonnegative singular value approximation (NSVA), which contributes to the robustness and sensitivity of the following DE analysis by taking advantages of the built-in characteristics of RNA-Seq count data.As a biomarker discovery algorithm built on network marker topology, the proposed SEQ-Marker not only bridges transcriptomics and systems biology but also contributes to clinical diagnostics.

View Article: PubMed Central - PubMed

Affiliation: Department of Computer and Information Science, Fordham University, New York, NY, USA. ; Quantitative Proteomics Center, Columbia University, New York, NY, USA.

ABSTRACT
As a revolutionary way to unveil transcription, RNA-Seq technologies are challenging bioinformatics for its large data volumes and complexities. A large number of computational models have been proposed for differential expression (DE) analysis and normalization from different standing points. However, there were no studies available yet to conduct disease biomarker discovery for this type of high-resolution digital gene expression data, which will actually be essential to explore its potential in clinical bioinformatics. Although there were many biomarker discovery algorithms available in traditional omics communities, they cannot be applied to RNA-Seq count data to seek biomarkers directly for its special characteristics. In this work, we have presented a biomarker discovery algorithm, SEQ-Marker for RNA-Seq data, which is built on a novel data-driven feature selection algorithm, nonnegative singular value approximation (NSVA), which contributes to the robustness and sensitivity of the following DE analysis by taking advantages of the built-in characteristics of RNA-Seq count data. As a biomarker discovery algorithm built on network marker topology, the proposed SEQ-Marker not only bridges transcriptomics and systems biology but also contributes to clinical diagnostics.

No MeSH data available.


The network marker with 102 genes and 194 interactions identified by the SEQ-Marker algorithm for Kidney–Liver data. The five core genes with the largest interactions (degrees) were emphasized in the network topology.
© Copyright Policy - open-access
Related In: Results  -  Collection


getmorefigures.php?uid=PMC4216051&req=5

f6-cin-suppl.1-2014-081: The network marker with 102 genes and 194 interactions identified by the SEQ-Marker algorithm for Kidney–Liver data. The five core genes with the largest interactions (degrees) were emphasized in the network topology.

Mentions: In addition, we obtained five network markers with scores 8.219, 7.922, 7.754, 7.735, and 7.637, respectively from jActiveModule, which were further merged to a “single” network marker with 102 genes and 194 interactions. We then identified that there are k = 5 core genes FN1, ESR1, HSB90 A, HSPB1, and CTNNB1 related to liver and kidney diseases by examining the genes with the largest interactions in the network marker. Figure 6 illustrated the network marker where the core genes with the largest interactions (degrees) were emphasized in the network topology.


Disease Biomarker Query from RNA-Seq Data.

Han H, Jiang X - Cancer Inform (2014)

The network marker with 102 genes and 194 interactions identified by the SEQ-Marker algorithm for Kidney–Liver data. The five core genes with the largest interactions (degrees) were emphasized in the network topology.
© Copyright Policy - open-access
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC4216051&req=5

f6-cin-suppl.1-2014-081: The network marker with 102 genes and 194 interactions identified by the SEQ-Marker algorithm for Kidney–Liver data. The five core genes with the largest interactions (degrees) were emphasized in the network topology.
Mentions: In addition, we obtained five network markers with scores 8.219, 7.922, 7.754, 7.735, and 7.637, respectively from jActiveModule, which were further merged to a “single” network marker with 102 genes and 194 interactions. We then identified that there are k = 5 core genes FN1, ESR1, HSB90 A, HSPB1, and CTNNB1 related to liver and kidney diseases by examining the genes with the largest interactions in the network marker. Figure 6 illustrated the network marker where the core genes with the largest interactions (degrees) were emphasized in the network topology.

Bottom Line: Although there were many biomarker discovery algorithms available in traditional omics communities, they cannot be applied to RNA-Seq count data to seek biomarkers directly for its special characteristics.In this work, we have presented a biomarker discovery algorithm, SEQ-Marker for RNA-Seq data, which is built on a novel data-driven feature selection algorithm, nonnegative singular value approximation (NSVA), which contributes to the robustness and sensitivity of the following DE analysis by taking advantages of the built-in characteristics of RNA-Seq count data.As a biomarker discovery algorithm built on network marker topology, the proposed SEQ-Marker not only bridges transcriptomics and systems biology but also contributes to clinical diagnostics.

View Article: PubMed Central - PubMed

Affiliation: Department of Computer and Information Science, Fordham University, New York, NY, USA. ; Quantitative Proteomics Center, Columbia University, New York, NY, USA.

ABSTRACT
As a revolutionary way to unveil transcription, RNA-Seq technologies are challenging bioinformatics for its large data volumes and complexities. A large number of computational models have been proposed for differential expression (DE) analysis and normalization from different standing points. However, there were no studies available yet to conduct disease biomarker discovery for this type of high-resolution digital gene expression data, which will actually be essential to explore its potential in clinical bioinformatics. Although there were many biomarker discovery algorithms available in traditional omics communities, they cannot be applied to RNA-Seq count data to seek biomarkers directly for its special characteristics. In this work, we have presented a biomarker discovery algorithm, SEQ-Marker for RNA-Seq data, which is built on a novel data-driven feature selection algorithm, nonnegative singular value approximation (NSVA), which contributes to the robustness and sensitivity of the following DE analysis by taking advantages of the built-in characteristics of RNA-Seq count data. As a biomarker discovery algorithm built on network marker topology, the proposed SEQ-Marker not only bridges transcriptomics and systems biology but also contributes to clinical diagnostics.

No MeSH data available.