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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.
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Noncompartmental analyses offer an alternative method for describing drug pharmacokinetics without relying on a specific compartmental model. In this approach, the drug's pharmacokinetics are assumed to be linear, with the terminal phase log-linear. This assumption allows for simplified analysis and interpretation of the drug's behavior in the body.
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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...
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When we take repeated measurements on the same or replicated samples, we will observe inconsistencies in the magnitude. These inconsistencies are called errors. To categorize and characterize these results and their errors, the researcher can use statistical analysis to determine the quality of the measurements and/or suitability of the methods.
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Related Experiment Video

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An integrative data analysis platform for gene set analysis and knowledge discovery in a data warehouse framework.

Yi-An Chen1, Lokesh P Tripathi1, Kenji Mizuguchi1

  • 1National Institutes of Biomedical Innovation, Health and Nutrition, 7-6-8 Saito-Asagi, Ibaraki, Osaka 567-0085, Japan kenji@nibiohn.go.jp.

Database : the Journal of Biological Databases and Curation
|March 19, 2016
PubMed
Summary

TargetMine has been upgraded with new data types and an interactive toolkit to enhance drug discovery and disease biology research. This user-friendly platform simplifies complex biological data analysis and visualization for knowledge discovery.

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Area of Science:

  • Bioinformatics
  • Drug Discovery
  • Systems Biology

Background:

  • Data analysis is crucial but challenging in drug discovery and disease biology.
  • Integrated data warehouses aid in understanding complex biological data.
  • TargetMine was previously developed for target prioritization.

Purpose of the Study:

  • To enhance TargetMine's capabilities beyond target prioritization for broader knowledge discovery.
  • To introduce an auxiliary toolkit for interactive data analysis and visualization.
  • To enable users to perform complex searches without programming.

Main Methods:

  • Upgraded and newly modeled data types within the TargetMine data warehouse.
  • Development of an interactive toolkit for data analysis and visualization.
  • Integration of biological and chemical data for drug discovery.

Main Results:

  • TargetMine now surveys a wider biological and chemical data space.
  • The toolkit allows interactive querying and analysis of compiled biological data.
  • Users can generate simplified, meaningful outputs for knowledge discovery.

Conclusions:

  • The enhanced TargetMine system facilitates hypothesis generation and discovery.
  • It provides a user-friendly interface for complex biological data analysis.
  • This resource supports both experimental and computational biologists in drug discovery research.