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Related Concept Videos

What is Gene Expression?01:42

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Gene expression is the process in which DNA directs the synthesis of functional products, that is, proteins. Cells can regulate gene expression at various stages. It allows organisms to generate different cell types and enables cells to adapt to internal and external factors.
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Sample Preparation and Analysis of RNASeq-based Gene Expression Data from Zebrafish
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To select relevant features for longitudinal gene expression data by extending a pathway analysis method.

Suyan Tian1, Chi Wang2, Howard H Chang3

  • 1Division of Clinical Research, The First Hospital of Jilin University, Changchun, Jilin, 130021, China.

F1000Research
|October 2, 2018
PubMed
Summary
This summary is machine-generated.

This study introduces a new pathway-based feature selection method for longitudinal omics data. The two-level SAMGSR algorithm effectively identifies relevant genes by treating gene expression over time as a gene set.

Keywords:
Core subset; feature selection; gene set analysis; longitudinal microarray data; significance analysis of microarray (SAM)

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Pathway-based feature selection integrates biological pathway information for gene selection.
  • Longitudinal omics data analysis requires specialized feature selection techniques.
  • Gene set analysis methods are increasingly important in biological data interpretation.

Purpose of the Study:

  • To adapt a gene set analysis method for feature selection in longitudinal microarray data.
  • To propose a novel pathway-based feature selection algorithm, the two-level SAMGSR method.
  • To demonstrate the utility of gene expression profiles over time as gene sets.

Main Methods:

  • Adaptation of the Significance Analysis of Microarray Gene Set Reduction (SAMGSR) algorithm.
  • Development of a two-level SAMGSR method for pathway-based feature selection.
  • Validation using simulated datasets and a real-world application.

Main Results:

  • Gene expression profiles over time can be effectively treated as gene sets.
  • The proposed two-level SAMGSR method successfully performs feature selection on longitudinal data.
  • Demonstrated the applicability of gene set analysis principles to longitudinal omics data.

Conclusions:

  • Gene set analysis methods can be modified for feature selection in longitudinal omics data.
  • The two-level SAMGSR method offers a novel approach for identifying relevant genes in time-course experiments.
  • This research bridges the gap between feature selection and gene set analysis, paving the way for future algorithm development.