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Methodology for Developing Life Tables for Sessile Insects in the Field Using the Whitefly, Bemisia tabaci, in Cotton As a Model System
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Knowledge-based data analysis comes of age.

Michael F Ochs1

  • 1Division of Oncology Biostatistics and Bioinformatics, 550 North Broadway, Suite 1103, Johns Hopkins University, Baltimore, MD 21205, USA. mfo@jhu.edu

Briefings in Bioinformatics
|October 27, 2009
PubMed
Summary
This summary is machine-generated.

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High-throughput biological data analysis requires methods tailored to specific systems and the

Area of Science:

  • Biological data analysis
  • High-throughput technologies
  • Computational biology

Background:

  • High-throughput technologies generate complex biological data, posing interpretation challenges.
  • Standard statistical methods often fail with large datasets (large-p, small-n problem).
  • Overfitting is a risk for computational learning algorithms in this context.

Purpose of the Study:

  • To review emerging analysis techniques for high-throughput biological data.
  • To highlight methods addressing the 'large-p, small-n' challenge.
  • To showcase how tailored analysis yields novel biological insights.

Main Methods:

  • Review of advanced statistical and computational techniques.
  • Focus on methods matching mathematical structure to biological systems.

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Last Updated: Jun 19, 2026

Methodology for Developing Life Tables for Sessile Insects in the Field Using the Whitefly, Bemisia tabaci, in Cotton As a Model System
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Methodology for Developing Life Tables for Sessile Insects in the Field Using the Whitefly, Bemisia tabaci, in Cotton As a Model System

Published on: November 1, 2017

Mass-Rearing and Molecular Studies in Tortricidae Pest Insects
06:22

Mass-Rearing and Molecular Studies in Tortricidae Pest Insects

Published on: March 25, 2022

  • Integration of data and prior biological knowledge.
  • Application of simple biological models in analysis.

    • Main Results:

    • Novel biological insights have been gained through these advanced techniques.
    • Techniques aligning mathematical models with biological systems improve data interpretation.
    • Addressing the 'large-p, small-n' problem is crucial for reliable inference.

    Conclusions:

    • Tailored analysis techniques are essential for interpreting high-throughput biological data.
    • Integrating diverse data and prior knowledge enhances statistical analysis.
    • Emerging methods utilizing simple biological models offer powerful new insights.