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Related Concept Videos

DNA Microarrays02:34

DNA Microarrays

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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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Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
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Computational Modeling and Analysis of Microarray Data: New Horizons.

Heather J Ruskin1

  • 1Sci-Sym Centre (Scientific Computing & Complex Systems Modelling), School of Computing, Dublin City University, Dublin 9, Ireland. Heather.Ruskin@dcu.ie.

Microarrays (Basel, Switzerland)
|October 25, 2016
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Summary
This summary is machine-generated.

Microarray data, despite the rise of sequencing, offers valuable insights for biomedical research. Novel methods are emerging to integrate microarray data with sequencing for enhanced biological understanding and disease pathogenesis studies.

Keywords:
augmentationhigh-throughput genomic analysisintegrationmicroarray data typesmodeling gene expressionsequencing techniques

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Area of Science:

  • Biomedical research
  • Genomic technologies
  • High-throughput data analysis

Background:

  • Microarray technologies have been a cornerstone of genomic research due to cost-effectiveness and broad applicability.
  • Advancements in microarray technology have improved data quality and interpretation over decades.
  • Early genomic research heavily relied on microarrays for generating vast datasets.

Discussion:

  • The emergence of next-generation sequencing (NGS) has led to a shift in genomic research due to reduced costs and improved data precision.
  • Sequencing data is often considered less noisy and more directly interpretable for species information compared to microarrays.
  • Despite the shift to sequencing, microarrays continue to generate new data and possess a significant legacy dataset.

Key Insights:

  • Microarray data, though facing competition from sequencing, remains relevant and offers a complementary perspective.
  • Integrating legacy and newly generated microarray data with sequencing data can provide added value.
  • Novel analytical methods are crucial for enhancing and combining data from different high-throughput techniques.

Outlook:

  • Future research will focus on developing advanced methods for data augmentation and integration.
  • Combining microarray and sequencing data promises new horizons in understanding biological systems and disease.
  • The Special Issue explores innovative approaches to maximize the utility of diverse high-throughput data.