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Related Concept Videos

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...
Cis-regulatory Sequences02:02

Cis-regulatory Sequences

Cis-regulatory sequences are short fragments of non-coding DNA that are present on the same chromosomes as the genes that they regulate. These fragments serve as binding sites for transcriptional regulators, proteins that are responsible for controlling gene transcription and differential gene expression across cell types in eukaryotes. Cis-regulatory sequences can be close to the gene of interest or thousands of bases away in the DNA sequence; however, those sequences that are further away are...
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Electrocyclic reactions, cycloadditions, and sigmatropic rearrangements are concerted pericyclic reactions that proceed via a cyclic transition state. These reactions are stereospecific and regioselective. The stereochemistry of the products depends on the symmetry characteristics of the interacting orbitals and the reaction conditions. Accordingly, pericyclic reactions are classified as either symmetry-allowed or symmetry-forbidden. Woodward and Hoffmann presented the selection criteria for...

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Large-Scale Multi-Omics Genome-Wide Association Studies (Mo-GWAS): Guidelines for Sample Preparation and Normalization
08:27

Large-Scale Multi-Omics Genome-Wide Association Studies (Mo-GWAS): Guidelines for Sample Preparation and Normalization

Published on: July 27, 2021

Discovering relational-based association rules with multiple minimum supports on microarray datasets.

Yu-Cheng Liu1, Chun-Pei Cheng, Vincent S Tseng

  • 1Department of Computer Science and Information Engineering and Institute of Medical Informatics, National Cheng Kung University, Taiwan.

Bioinformatics (Oxford, England)
|September 20, 2011
PubMed
Summary
This summary is machine-generated.

The REMMAR algorithm enhances gene expression analysis by considering gene pair importance and relation intensity. This method discovers more biologically relevant association rules with higher precision than traditional approaches.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Association rule analysis is crucial for identifying gene expression relationships.
  • Existing methods often overlook individual gene importance and relation intensity.
  • There's a need for methods that account for the varying significance of gene interactions.

Purpose of the Study:

  • To introduce the Relational-based Multiple Minimum Supports Association Rules (REMMAR) algorithm.
  • To address the limitations of previous association rule methods in gene expression analysis.
  • To discover more biologically meaningful association rules by incorporating relation intensity.

Main Methods:

  • Developed the REMMAR algorithm, which adjusts minimum relation support (MRS) based on gene pair regulatory relation intensity.
  • Utilized the shortest distance in the Saccharomyces cerevisiae gene regulatory network (GRN) as relation intensity.
  • Applied REMMAR to two S. cerevisiae gene expression datasets.

Main Results:

  • REMMAR generated more association rules with stronger relation intensity compared to traditional methods.
  • The algorithm effectively filtered out rules lacking biological meaning in the protein-protein interaction network (PPIN).
  • REMMAR achieved 100% precision, outperforming the Apriori method's 87.5% precision in a literature survey validation.

Conclusions:

  • The REMMAR algorithm successfully discovers stronger association rules with enhanced biological relevance.
  • It provides a valuable tool for biologists in complex genetic exploration by improving upon traditional methods.
  • Incorporating relation intensity significantly enhances the discovery of meaningful gene expression associations.