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Related Concept Videos

Protein Organization01:13

Protein Organization

Overview

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Related Experiment Video

Updated: Jun 2, 2026

Enriching Subcellular Proteins in Leptospira Using a Triton X-114-Based Fractionation Approach
04:25

Enriching Subcellular Proteins in Leptospira Using a Triton X-114-Based Fractionation Approach

Published on: August 8, 2025

Model building and intelligent acquisition with application to protein subcellular location classification.

C Jackson1, E Glory-Afshar, R F Murphy

  • 1Center for Bioimage Informatics, Department of Biomedical Engineering, Carnegie Mellon University, 5000 Forbes Ave., Pittsburgh, PA 15213, USA.

Bioinformatics (Oxford, England)
|May 12, 2011
PubMed
Summary

This study introduces a new framework for acquiring protein subcellular location data, optimizing cell and frame numbers during data collection. This intelligent acquisition reduces time and storage needs without compromising classification accuracy.

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Multi-color Localization Microscopy of Single Membrane Proteins in Organelles of Live Mammalian Cells
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Area of Science:

  • Cell Biology
  • Biophysics
  • Computational Biology

Background:

  • Accurate protein subcellular localization is crucial for understanding cellular functions.
  • Current methods for acquiring microscopy data can be time-consuming and lead to photobleaching.
  • Building models of protein localization patterns requires extensive data collection across various conditions.

Purpose of the Study:

  • To develop a framework for intelligent acquisition of protein subcellular location patterns.
  • To minimize acquisition time and photobleaching while building accurate models.
  • To automatically adapt the data acquisition process based on learned models.

Main Methods:

  • Developed a framework and algorithms for real-time model learning during image acquisition.
  • Integrated model building directly into the acquisition process, rather than as a post-processing step.
  • Simultaneously determined optimal numbers of cells and frames per cell for acquisition.

Main Results:

  • Validated the framework on protein subcellular location classification tasks.
  • Demonstrated significant time and storage savings compared to traditional methods.
  • Achieved comparable or improved classification accuracy with reduced acquisition efforts.

Conclusions:

  • Intelligent acquisition frameworks can streamline the process of building protein localization models.
  • Integrating model learning during acquisition optimizes resource utilization (time, storage).
  • This approach enhances efficiency in biological imaging without sacrificing data quality.