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Quantitative Framework for Bench-to-Bedside Cancer Research.

Aubhishek Zaman1,2, Trever G Bivona1,2,3

  • 1Department of Medicine, University of California, San Francisco, CA 94158, USA.

Cancers
|November 11, 2022
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Summary
This summary is machine-generated.

Quantitative bioscience research, especially in cancer OMICS, requires standardized frameworks. This study provides guidelines for quantitative cancer research and therapy to improve experimental design and data consistency.

Keywords:
IC50OMICSbench-to-bedsidechemical biologyhigh throughput screen (HTS)quantitative biology

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Area of Science:

  • Quantitative bioscience
  • Cancer research
  • Chemical biology

Background:

  • Bioscience is increasingly data-intensive, necessitating mathematical and quantitative approaches.
  • A shift towards quantitative descriptions of cells and phenotypes offers opportunities in cancer OMICS, chemical biology, and pharmacology.
  • A lack of standardized frameworks hinders quantitative cancer research.

Purpose of the Study:

  • To present a basic framework and guidelines for quantitative cancer research and therapy.
  • To outline concepts and use cases for chemical-protein interactions in quantitative research.
  • To provide recommendations for assay setup and catalog applications of quantitative techniques.

Main Methods:

  • Developing a framework for quantitative cancer research.
  • Outlining concepts and use cases for chemical-protein interactions.
  • Recommending assay setup and conditions.
  • Cataloging applications in discovery pipelines and analytical methods.

Main Results:

  • A proposed framework and guidelines for quantitative cancer research and therapy.
  • Examples of quantitative concepts applied to chemical-protein interactions.
  • Recommendations for improving experimental design and assay conditions.
  • Catalog of quantitative techniques for cancer research pipelines.

Conclusions:

  • Adherence to the proposed guidelines will enhance experimental design in quantitative cancer research.
  • Standardized quantitative datasets will reduce variability in bioscience research.
  • The framework supports the advancement of bench-to-bedside quantitative cancer therapy.