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RNA-seq03:21

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RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases. 
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Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress
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Clustering of High Throughput Gene Expression Data.

Harun Pirim1, Burak Ekşioğlu, Andy Perkins

  • 1Department of Industrial and Systems Engineering, Mississippi State University, P.O. Box 9542, Mississippi State, MS 39762.

Computers & Operations Research
|November 13, 2012
PubMed
Summary
This summary is machine-generated.

This review covers clustering algorithms for gene expression data analysis. It highlights bioinformatics challenges and introduces gene expression data clustering to operations research.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Systems Biology

Background:

  • High-throughput biological data analysis is crucial for life sciences.
  • Computational methods are essential for processing and interpreting complex biological datasets.
  • Clustering is a key technique for understanding biological processes, especially at the genomics level.

Purpose of the Study:

  • To review current clustering algorithms specifically for gene expression data analysis.
  • To introduce the problem of gene expression data clustering to the operations research community.
  • To bridge the gap between bioinformatics and operations research in biological data analysis.

Main Methods:

  • Review of existing literature on clustering algorithms for gene expression data.
  • Identification and categorization of prevalent clustering techniques.
  • Discussion of challenges and considerations in applying clustering to genomics data.

Main Results:

  • Overview of various clustering algorithms applicable to gene expression data.
  • Highlighting the specific requirements and challenges of gene expression data analysis.
  • Establishing gene expression data clustering as a significant problem within bioinformatics.

Conclusions:

  • Clustering is a vital tool for analyzing gene expression data in bioinformatics.
  • Further research and application of operations research methodologies can advance gene expression data analysis.
  • This review serves as an introduction for operations research professionals to a critical bioinformatics problem.