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Related Concept Videos

Protein-protein Interfaces02:04

Protein-protein Interfaces

Many proteins form complexes to carry out their functions, making protein-protein interactions (PPIs) essential for an organism's survival. Most PPIs are stabilized by numerous weak noncovalent chemical forces. The physical shape of the interfaces determines the way two proteins interact. Many globular proteins have closely-matching shapes on their surfaces, which form a large number of weak bonds. Additionally, many PPIs occur between two helices or between a surface cleft and a polypeptide...
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Mapping Dysfunctional Protein-Protein Interactions in Disease
09:39

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A false-discovery-rate-based loss framework for selection of interactions.

Wei Chen1, Debashis Ghosh, Trivellore E Raghunathan

  • 1Karmanos Cancer Institute, 4160 John R, Detroit, MI 48201, U.S.A. chenw@karmanos.org

Statistics in Medicine
|November 6, 2007
PubMed
Summary

This study introduces new Bayesian methods for selecting important interaction effects in complex datasets, addressing challenges like limited sample sizes and multiple testing. The findings improve the reliable identification of interactions in scientific research.

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Area of Science:

  • Statistical modeling
  • Bioinformatics
  • Genomics

Background:

  • Interaction effects are crucial for explaining outcome variations across scientific fields.
  • Selecting interactions is challenging due to small sample sizes, interpretability concerns, and multiple-testing issues.
  • A lack of robust algorithms hinders the exploration of significant interaction effects.

Purpose of the Study:

  • To investigate challenges in selecting interaction effects.
  • To develop statistically sound algorithms for automatic variable selection with interactions.
  • To control False Discovery Rate (FDR) in a Bayesian framework.

Main Methods:

  • Exploration of the model space for interaction selection.
  • Development of hypothesis-testing procedures for interactions.
  • Proposal of Bayesian loss functions and decision rules to control FDR.
  • Simulation studies to compare power and FDR performance.

Main Results:

  • Proposed Bayesian decision rules effectively control FDR.
  • Simulations demonstrate favorable performance in terms of power and FDR control.
  • Methods are validated using real-world data from cancer studies.

Conclusions:

  • The developed methods offer a statistically sound approach to interaction selection.
  • These Bayesian techniques address key challenges in identifying significant interactions.
  • The approach enhances the reliable discovery of gene expression and treatment interactions in complex biological data.