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Related Concept Videos

Cluster Sampling Method01:20

Cluster Sampling Method

Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...
RNA-seq03:21

RNA-seq

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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DNA Microarrays02:34

DNA Microarrays

Microarrays are high-throughput and relatively inexpensive assays that can be automated to analyze large quantities of data at a time. They are used in genome-wide studies to compare gene or protein expression under two varied conditions, such as healthy and diseased states. Microarrays consist of glass or silica slides on which probe molecules are covalently attached through surface functionalization. Most commonly, the slides are prepared through the chemisorption of silanes to silica...
Model-Independent Approaches for Pharmacokinetic Data: Noncompartmental Analysis00:59

Model-Independent Approaches for Pharmacokinetic Data: Noncompartmental Analysis

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Related Experiment Video

Updated: Jun 22, 2026

Measuring mRNA Levels Over Time During the Yeast S. cerevisiae Hypoxic Response
09:45

Measuring mRNA Levels Over Time During the Yeast S. cerevisiae Hypoxic Response

Published on: August 10, 2017

A new approach for clustering gene expression time series data.

Rosy Das1, Jugal Kalita, Dhruba K Bhattacharyya

  • 1Department of Computer Science and Engineering, Tezpur University, Napaam 784028, Assam, India. rosy8@tezu.ernet.in

International Journal of Bioinformatics Research and Applications
|June 16, 2009
PubMed
Summary

This study introduces a novel dissimilarity measure and a graph-based clustering method for analyzing gene expression time series data. The new approach effectively identifies gene groups with similar expression patterns, outperforming existing methods.

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Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress
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Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress
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Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress

Published on: July 29, 2022

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Systems Biology

Background:

  • Analyzing gene expression time series data is vital for understanding biological processes.
  • Selecting appropriate similarity measures is critical for accurate clustering of gene expression profiles.

Purpose of the Study:

  • To propose a novel dissimilarity measure tailored for gene expression time series data.
  • To develop and evaluate a graph-based clustering method utilizing the new dissimilarity measure.

Main Methods:

  • Development of a new dissimilarity measure for gene expression profiles.
  • Implementation of a graph-based clustering algorithm.
  • Comparative analysis with existing similarity measures.

Main Results:

  • The proposed dissimilarity measure demonstrates effectiveness in capturing similarities between gene expression profiles.
  • The graph-based clustering method, using the new measure, performs satisfactorily on real-life datasets.
  • The new dissimilarity measure is found to be superior to other commonly used measures.

Conclusions:

  • The novel dissimilarity measure and graph-based clustering method offer a robust approach for gene expression time series analysis.
  • This methodology aids in identifying functionally related genes based on their expression dynamics.
  • The findings contribute to improved understanding of gene regulation and biological pathways.