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Related Experiment Video

Updated: Jan 13, 2026

Using Human Differentially Expressed Gene Lists to Perform Downstream Pathway Enrichment Analysis and Target Prioritization

Using Human Differentially Expressed Gene Lists to Perform Downstream Pathway Enrichment Analysis and Target Prioritization

Published on: October 3, 2025

Disease Biomarker Query from RNA-Seq Data.

Henry Han¹, Xiaoqian Jiang²

¹Department of Computer and Information Science, Fordham University, New York, NY, USA. ; Quantitative Proteomics Center, Columbia University, New York, NY, USA.

Cancer Informatics

|November 14, 2014

Summary

This summary is machine-generated.

This study introduces SEQ-Marker, a novel algorithm for RNA-Seq data, enabling disease biomarker discovery. It enhances differential expression analysis for clinical bioinformatics applications.

Keywords:

RNA-Seq biomarker discovery feature selection

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Identification of Circular RNAs using RNA Sequencing

Identification of Circular RNAs using RNA Sequencing

Published on: November 14, 2019

Area of Science:

Bioinformatics
Genomics
Systems Biology

Background:

RNA-Seq technologies generate large, complex datasets challenging traditional bioinformatics.
Existing biomarker discovery algorithms are not directly applicable to RNA-Seq count data.
Clinical bioinformatics requires robust methods for disease biomarker discovery from high-resolution gene expression data.

Purpose of the Study:

To present SEQ-Marker, a novel biomarker discovery algorithm specifically designed for RNA-Seq data.
To address the limitations of existing methods in handling the unique characteristics of RNA-Seq count data.
To facilitate disease biomarker discovery and enhance clinical diagnostics using transcriptomic data.

Main Methods:

Development of SEQ-Marker, a biomarker discovery algorithm for RNA-Seq data.
Integration of a novel data-driven feature selection algorithm, nonnegative singular value approximation (NSVA).
Leveraging network marker topology for biomarker identification.

Main Results:

SEQ-Marker demonstrates robustness and sensitivity in differential expression analysis.
The algorithm effectively utilizes the inherent characteristics of RNA-Seq count data.
SEQ-Marker bridges transcriptomics and systems biology for improved clinical applications.

Conclusions:

SEQ-Marker provides a new approach for disease biomarker discovery from RNA-Seq data.
The algorithm enhances the potential of transcriptomic data in clinical bioinformatics.
SEQ-Marker contributes to advancing systems biology and clinical diagnostics.