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Related Concept Videos

Genomics02:02

Genomics

Genomics is the science of genomes: it is the study of all the genetic material of an organism. In humans, the genome consists of information carried in 23 pairs of chromosomes in the nucleus, as well as mitochondrial DNA. In genomics, both coding and non-coding DNA is sequenced and analyzed. Genomics allows a better understanding of all living things, their evolution, and their diversity. It has a myriad of uses: for example, to build phylogenetic trees, to improve productivity and...
Genome Annotation and Assembly03:36

Genome Annotation and Assembly

The genome refers to all of the genetic material in an organism. It can range from a few million base pairs in microbial cells to several billion base pairs in many eukaryotic organisms. Genome assembly refers to the process of taking the DNA sequencing data and putting it all back together in a correct order to create a close representation of the original genome. This is followed by the identification of functional elements on the newly assembled genome, a process called genome annotation.
Synthetic Biology02:55

Synthetic Biology

Synthetic biology is an interdisciplinary science that involves using principles from disciplines such as engineering, molecular biology, cell biology, and systems biology. It involves remodeling existing organisms from nature or constructing completely new synthetic organisms for applications such as protein or enzyme production, bioremediation, value-added macromolecule production, and the addition of desirable traits to crops, to name a few.
Golden rice
Golden rice is a genetically modified...
Combinatorial Gene Control02:33

Combinatorial Gene Control

Combinatorial gene control is the synergistic action of several transcriptional factors to regulate the expression of a single gene. The absence of one or more of these factors may lead to a significant difference in the level of gene expression or repression.
The expression of more than 30,000 genes is controlled by approximately 2000-3000 transcription factors. This is possible because a single transcription factor can recognize more than one regulatory sequence. The specificity in gene...
Genome-wide Association Studies-GWAS01:11

Genome-wide Association Studies-GWAS

Genome-wide association studies or GWAS are used to identify whether common SNPs are associated with certain diseases. Suppose specific SNPs are more frequently observed in individuals with a particular disease than those without the disease. In that case, those SNPs are said to be associated with the disease. Chi-square analysis is performed to check the probability of the allele likely to be associated with the disease.
GWAS does not require the identification of the target gene involved in...
Organization of Genes02:07

Organization of Genes

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Related Experiment Video

Updated: Jun 23, 2026

Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases
07:41

Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases

Published on: May 17, 2019

A cube framework for incorporating inter-gene information into biological data mining.

Kuan-ming Lin1, Jaewoo Kang, Hanjun Shin

  • 1Department of Computer Science, Duke University, Durham, North Carolina 27708, USA. km@cs.duke.edu

International Journal of Data Mining and Bioinformatics
|May 13, 2009
PubMed
Summary
This summary is machine-generated.

This study introduces a new framework for integrating heterogeneous microarray data, unlocking the full potential of public repositories. The method leverages invariant gene features for high-quality data fusion, improving resource utilization.

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Last Updated: Jun 23, 2026

Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases
07:41

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Published on: May 17, 2019

Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations
08:03

Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations

Published on: December 7, 2021

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Public repositories like the Gene Expression Omnibus (GEO) and the Stanford Microarray Database (SMD) house vast amounts of microarray data.
  • The heterogeneity of experimental designs prevents direct integration of data from different microarray experiments.
  • This underutilization limits the community's ability to fully leverage these valuable genomic resources.

Purpose of the Study:

  • To develop a novel framework for integrating heterogeneous microarray data.
  • To enable high-quality data integration from diverse experimental sources.
  • To enhance the utility of public microarray data repositories.

Main Methods:

  • Proposed a new microarray integration framework.
  • Exploited invariant features, specifically the relative information among genes, for data integration.
  • Demonstrated the generalization capabilities of the proposed approach over previous methods.

Main Results:

  • Achieved high-quality integration of heterogeneous microarray data.
  • Successfully utilized invariant features to overcome data heterogeneity.
  • Showcased that the new framework generalizes previous integration approaches.

Conclusions:

  • The proposed framework effectively integrates diverse microarray datasets.
  • Exploiting relative gene information is key to successful data fusion.
  • This approach significantly enhances the usability of public microarray data resources.