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Related Experiment Video

Updated: Mar 2, 2026

Author Spotlight: Advanced Integrated Model for Sepsis-Induced Myopathy and Single-Cell Metabolic Analysis
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Analysis and modelling of septic shock microarray data using Singular Value Decomposition.

Srinivas Allanki1, Madhulika Dixit1, Paul Thangaraj2

  • 1Laboratory of Vascular Biology, Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences Building, Indian Institute of Technology Madras, Chennai 600 036, India.

Journal of Biomedical Informatics
|May 14, 2017
PubMed
Summary
This summary is machine-generated.

This study introduces Singular Value Decomposition (SVD) for analyzing gene expression patterns in microarray data. A novel model predicts vital genes in disease progression, aiding in the discovery of new gene-based therapies for conditions like septic shock.

Keywords:
MicroarrayMicroarray modellingPredictive modellingSeptic shockSingular value decomposition

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Microarray data analysis requires efficient techniques beyond fold change and p-value for biological insights.
  • Predicting key genes in disease progression is crucial for developing targeted therapies.

Purpose of the Study:

  • To develop an unsupervised microarray data analysis tool using Singular Value Decomposition (SVD).
  • To create a novel mathematical model for predicting vital genes in disease progression.
  • To identify potential gene-based therapies for diseases like septic shock.

Main Methods:

  • Applied Singular Value Decomposition (SVD) for unsupervised analysis of gene expression patterns.
  • Developed a mathematical model using normal-distribution-based random number generation to predict vital genes.
  • Re-analyzed septic shock microarray data from Cazalis et al. (2014).

Main Results:

  • The SVD-based analysis provided additional information compared to conventional methods for septic shock data.
  • The novel model successfully predicted genes related to septic shock progression.
  • The predicted genes offer a focused set for researchers investigating disease mechanisms.

Conclusions:

  • SVD offers a powerful approach for uncovering hidden patterns in microarray data.
  • The developed predictive model aids in identifying critical genes for disease progression.
  • This work facilitates the discovery of novel gene-based therapeutic strategies.